Neural Magic jobs

Neural Nexus – Data Steward, Data & AI Enablement (AuRA) (Open) India - Hyderabad posted: Posted 30+ Days Ago

Neural Nexus – Data Steward, Data & AI Enablement (AuRA) India - Hyderabad posted: Posted 30+ Days Ago

Manager, Agentic AI Business Solutions, Neural Nexus India - Hyderabad posted: Posted 30+ Days Ago

Sr. Associate, Agentic AI Business Solutions, Neural Nexus India - Hyderabad posted: Posted 30+ Days Ago

Senior Applied ML Engineer, Graph Neural Network, ML Frontiers Zürich, Switzerland Google Cloud's mission is to make every business successful through AI by combining cutting-edge technology, infrastructure, and talent. AI/ML software engineers in Cloud bridge the gap between pioneering models and a massive product vehicle reaching billions. Our talent density and AI-powered tools drive rapid development, rooted in a culture of empowerment and a bias to action. In this role, you aren't just building technology; you're shaping the frontier of enterprise and driving the evolution of advanced models. In this role, you will help define new features based on client interactions and your own research. When a new feature or idea is identified, you will take technical leadership of it. This includes validating the idea through literature reviews and experiments, implementing it efficiently for target hardware (CPU, GPU, TPU, or distributed systems), and productionizing it into Graph Flow as an easy-to-use, modular component. Discovering new features and ideas requires constant exploration, brainstorming with our team and research collaborators, and gathering client feedback. You will also mentor and help grow other engineers, research students, and interns. Google Cloud accelerates every organization's ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems. Develop feature development (design, coding, doc writing, and maintenance),

Sr. SDM, AI Inference Technology, Neuron SDK Seattle, Washington, USA AWS Utility Computing (UC) provides product innovations - from foundational services such as Amazon Elastic Compute Cloud (EC2), to new product innovations that continue to set AWS's services and features apart in the industry. Come develop inference acceleration for AWS Neuron, the complete software stack for Trainium, Amazon's custom cloud-scale machine learning accelerators that power the latest AI models As the Sr. SDM for the Inference Technology Team, you will lead a strong team of managers and engineers to build fundamental inference technology building blocks and libraries to enable AI developers to optimize model for inference on Trainium and Inferentia devices. You will be responsible for the full development life cycle of inference library and feature development, including reliability and scalability. You will develop the Neuronx_Distributed Inference Libraries and contribute to other popular open source Inference Libraries, enabling customers to optimize LLMs, multimodal, and generative models. The ideal candidate will have an established background in delivering AI feature support for demanding, fast-changing priorities or delivering high-performance models using distributed inference libraries. The ideal candidate should have a strong technical ability to understand and manage a vertically integrated system stack that consisting of hardware, frameworks, and workflows. A day in the life You will work with the executive leadership and other senior management and technical leaders to define product directions and deliver them to customers. We build massive-scale distributed training and inference solutions, developing the full stack of software, servers and

Software Engineer II- AI/ML, AWS Neuron Seattle, Washington, USA The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX enabling unparalleled ML inference and training performance. The Training Enablement and Foundation team is at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for AWS's custom ML accelerators. Working across the stack from PyTorch till the hardware-software boundary, our engineers build systematic infrastructure, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine tuned for optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. As part of the broader Neuron organization, our team works across multiple technology layers - from frameworks and kernels and collaborate with compiler to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to work at the intersection of

ML Kernel Performance Engineer, AWS Neuron, Annapurna Labs Cupertino, California, USA The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium. The Acceleration Kernel Library team is at the forefront of maximizing performance for AWS's custom ML accelerators. Working at the hardware-software boundary, our engineers craft high-performance kernels for ML functions, ensuring every FLOP counts in delivering optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Inferentia and Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch, enabling unparalleled ML inference and training performance. As part of the broader Neuron Compiler organization, our team works across multiple technology layers - from frameworks and compilers to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to work at the intersection of machine learning, high-performance computing, and distributed architectures, where you'll help shape the future of AI acceleration technology This is an opportunity to work on cutting-edge products at the

Applied Scientist, Neuron ARG, Annapurna ML Seattle, Washington, USA The Automated Reasoning Group in the Amazon Neuron team is looking for an Applied Scientist to work on the intersection of Artificial Intelligence and program analysis to raise the code quality bar in our state-of-the-art deep learning compiler stack. This stack is designed to optimize application models across diverse domains, including Large Language and Vision, originating from leading frameworks such as PyTorch and JAX. Your role will involve working closely with our custom-built Machine Learning accelerator, Trainium, which represents the forefront of innovation for advanced ML capabilities, and is the underpinning of Generative AI. In this role as an Applied Scientist, you'll be instrumental in designing, developing, and deploying analyzers for ML compiler stages and compiler IRs. You will architect and implement business-critical tooling, publish research, and mentor a brilliant team of experienced scientists and engineers. You will need to be technically capable, credible, and curious in your own right as a trusted AWS Neuron engineer, innovating on behalf of our customers. Your responsibilities will involve tackling crucial challenges alongside a talented engineering team, contributing to leading-edge design and research in compiler technology and deep-learning systems software. Strong experience in programming languages, compilers, program analyzers, theorem provers, and program synthesis engines will be a benefit in this role. A background in machine learning and AI accelerators is preferred but not required. Basic Qualifications: - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience - Experience

Sr. Machine Learning - Compiler Engineer III, AWS Neuron, Annapurna Labs Cupertino, California, USA The Product: AWS Machine Learning accelerators are at the forefront of AWS innovation and one of several AWS tools used for building Generative AI on AWS. The Inferentia chip delivers best-in-class ML inference performance at the lowest cost in cloud. Trainium will deliver the best-in-class ML training performance with the most teraflops (TFLOPS) of compute power for ML in the cloud. This is all enabled by cutting edge software stack, the AWS Neuron Software Development Kit (SDK), which includes an ML compiler, runtime and natively integrates into popular ML frameworks, such as PyTorch, TensorFlow and MxNet. AWS Neuron and Inferentia are used at scale with customers like Snap, Autodesk, Amazon Alexa, Amazon Rekognition and more customers in various other segments. The Team: As a whole, the Amazon Annapurna Labs team is responsible for silicon development at AWS. The team covers multiple disciplines including silicon engineering, hardware design and verification, software and operations. The AWS Neuron team works to optimize the performance of complex neural net models on our custom-built AWS hardware. More specifically, the AWS Neuron team is developing a deep learning compiler stack that takes neural network descriptions created in frameworks such as TensorFlow, PyTorch, and MXNET, and converts them into code suitable for execution. As you might expect, the team is comprised of some of the brightest minds in the engineering, research, and product communities, focused on the ambitious goal of creating a toolchain

Machine Learning Engineer, AWS Neuron Inference, Annapurna ML Seattle, Washington, USA AWS Neuron is the complete software stack for the AWS Inferentia and Trainium cloud-scale machine learning accelerators and the Trn2 and future Trn3 servers that use them. This role is for a software engineer in the Machine Learning Applications (ML Apps) team for AWS Neuron. This role develops, enables and performance tunes building blocks for all key ML model families, including Llama3, GPT OSS, Qwen3, DeepSeek and beyond. The Neuron Inference Technology team works side by side with the Inference Model Enablement, compiler runtime engineers to create, build and tune high-performance distributed inference solutions for the latest generation Trainium accelerators. Experience optimizing LLM inference performance with kernels, Python, PyTorch or JAX is a must. Key job responsibilities This team develops optimized building blocks for the Neuron distributed inference library, tuning them to ensure highest performance and maximize efficiency running on Trn2 and Trn3 servers. A day in the life As you develop technology components, you'll create metrics, implement automation and other improvements, and resolve the root cause of software defects. You'll also participate in design discussions, code review, and communicate with internal and external stakeholders. You will work cross-functionally with teams across Neufon in a fast-paced startup-like development environment, where we constantly stay on top of the latest priorities as the AI landscape evolves. About the team Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an

Senior Software Engineer - AI/ML, AWS Neuron Inference Seattle, Washington, USA AWS Neuron is the complete software stack for the AWS Inferentia and Trainium cloud-scale machine learning accelerators. This role is for a senior software engineer in the Machine Learning Inference Applications team. This role is responsible for development and performance optimization of core building blocks of LLM Inference - Attention, MLP, Quantization, Speculative Decoding, Mixture of Experts, etc. The team works side by side with chip architects, compiler engineers and runtime engineers to deliver performance and accuracy on Neuron devices across a range of models. Key job responsibilities Responsibilities of this role include adapting latest research in LLM optimization to Neuron chips to extract best performance from both open source as well as internally developed models. Working across teams and organizations is key. About the team Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future. Basic Qualifications: - 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience - Bachelor's degree in computer science or equivalent - 5+ years of programming using a

Software Development Engineer - AI/ML, Amazon Neuron, Multimodal Inference Seattle, Washington, USA The Annapurna Labs team at Amazonbuilds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Inferentia and Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX enabling unparalleled ML inference and training performance. The Inference Enablement and Acceleration team is at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for AWS's custom ML accelerators. Working across the stack from PyTorch till the hardware-software boundary, our engineers build systematic infrastructure, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine tuned for optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. As part of the broader Neuron organization, our team works across multiple technology layers - from frameworks and kernels and collaborate with compiler to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to work at the intersection

Software Development Engineer AI/ML, Inference Serving, AWS Neuron Cupertino, California, USA AWS Neuron is the software stack powering AWS Inferentia and Trainium machine learning accelerators, designed to deliver high-performance, low-cost inference at scale. The Neuron Serving team develops infrastructure to serve modern machine learning models-including large language models (LLMs) and multimodal workloads-reliably and efficiently on AWS silicon. We are seeking a Software Development Engineer to lead and architect our next-generation model serving infrastructure, with a particular focus on large-scale generative AI applications. Key job responsibilities * Architect and lead the design of distributed ML serving systems optimized for generative AI workloads * Drive technical excellence in performance optimization and system reliability across the Neuron ecosystem * Design and implement scalable solutions for both offline and online inference workloads * Lead integration efforts with frameworks such as vLLM, SGLang, Torch XLA, TensorRT, and Triton * Develop and optimize system components for tensor/data parallelism and disaggregated serving * Implement and optimize custom PyTorch operators and NKI kernels * Mentor team members and provide technical leadership across multiple work streams * Drive architectural decisions that impact the entire Neuron serving stack * Collaborate with customers, product owners, and engineering teams to define technical strategy * Author technical documentation, design proposals, and architectural guidelines A day in the life You'll lead critical technical initiatives while mentoring team members. You'll collaborate with cross-functional teams of applied scientists, system engineers, and product managers to architect and deliver state-of-the-art inference capabilities. Your day might involve: * Leading

Software Development Engineer, AI/ML, AWS Neuron, Model Inference Cupertino, California, USA The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Inferentia and Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX enabling unparalleled ML inference and training performance. The Inference Enablement and Acceleration team is at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for AWS's custom ML accelerators. Working across the stack from PyTorch till the hardware-software boundary, our engineers build systematic infrastructure, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine tuned for optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. As part of the broader Neuron organization, our team works across multiple technology layers - from frameworks and kernels and collaborate with compiler to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to work

Sr. ML Kernel Performance Engineer, AWS Neuron, Annapurna Labs Cupertino, California, USA The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium. The Acceleration Kernel Library team is at the forefront of maximizing performance for AWS's custom ML accelerators. Working at the hardware-software boundary, our engineers craft high-performance kernels for ML functions, ensuring every FLOP counts in delivering optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Inferentia and Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch, enabling unparalleled ML inference and training performance. As part of the broader Neuron Compiler organization, our team works across multiple technology layers - from frameworks and compilers to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to work at the intersection of machine learning, high-performance computing, and distributed architectures, where you'll help shape the future of AI acceleration technology This is an opportunity to work on cutting-edge products at

Senior Software Development Engineer, AI/ML, AWS Neuron, Model Inference Cupertino, California, USA The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Inferentia and Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX enabling unparalleled ML inference and training performance. The Inference Enablement and Acceleration team is at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for AWS's custom ML accelerators. Working across the stack from PyTorch till the hardware-software boundary, our engineers build systematic infrastructure, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine tuned for optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. As part of the broader Neuron organization, our team works across multiple technology layers - from frameworks and kernels and collaborate with compiler to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to

Research Scientist, Robotics, Embodied AI, DeepMind Mountain View, CA, USA We believe there are many problems in the world in which robotics could play a significant role in making it easier, faster and safer for people to get things done. We're looking for roboticists, designers, hardware and software engineers to help us explore these possibilities, develop breakthrough technologies, and build new products that could help millions of people. At DeepMind Robotics we are pioneering bringing AI to the physical world. We are powering an era of physical agents, enabling robots to perceive, plan, think, use tools, and act to better solve tasks. In this role, you will develop Vision Language Action (VLA) models that combine Gemini's world understanding with physical actions to directly control robots. You will include Gemini Robotics (our Gemini model for the physical world), and Gemini Robotics On-Device (our Gemini model that runs without a data network). You will also develop reasoning and agentic systems for the physical world, including Gemini Robotics-ER, a Gemini agent with spatial understanding. You will enable robots to perform a range of tasks, respond interactively to their environment, achieve dexterity, and reason over long multi-step tasks. You will focus on advancing in areas of general purpose robotics, including real world understanding, action generalization, human robot interaction, whole-body control, and continual learning. Additionally, you will partner with robotics companies to bring this intelligence to applications at scale. Artificial intelligence will be one of humanity's most transformative inventions. At Google DeepMind, we are a

Senior Product Manager, AWS Neurosymbolic AI Boston, Massachusetts, USA The AWS Neurosymbolic AI team is pioneering the integration of formal reasoning and neural approaches to build AI systems that are not only powerful, but provably correct. We sit at one of the most compelling frontiers in computer science: the convergence of neural networks and symbolic reasoning, where large language models meet theorem provers, and where probabilistic intelligence meets mathematical certainty. Our mission is to make AI trustworthy at scale. We develop technology that enables AI systems to reason rigorously, verify their own outputs, and provide mathematical guarantees about their behavior. This is a fundamental shift in how AI systems are built, and we believe it's on the critical path to the next generation of safe, reliable AI-powered applications. We are one of the strongest concentrations of neurosymbolic AI talent in industry. Our team includes original contributors to the Lean theorem prover and is advised by Lean's Chief Architect. We bring together researchers and engineers from both the AI and formal methods communities, a combination that is extraordinarily rare and increasingly essential. We build on Amazon's 10+ year track record of bringing automated reasoning to production at scale. AWS pioneered the use of formal methods in cloud infrastructure, from network reachability analysis to cryptographic protocol verification to access policy reasoning, systems that serve hundreds of millions of customers today. Now we're taking the next giant leap: fusing that heritage with frontier AI to make every AI system verifiable, trustworthy, and safe.

Principal Product Manager, AWS Neurosymbolic AI Seattle, Washington, USA The AWS Neurosymbolic AI team is pioneering the integration of formal reasoning and neural approaches to build AI systems that are not only powerful, but provably correct. We sit at one of the most compelling frontiers in computer science: the convergence of neural networks and symbolic reasoning, where large language models meet theorem provers, and where probabilistic intelligence meets mathematical certainty. Our mission is to make AI trustworthy at scale. We develop technology that enables AI systems to reason rigorously, verify their own outputs, and provide mathematical guarantees about their behavior. This is a fundamental shift in how AI systems are built, and we believe it's on the critical path to the next generation of safe, reliable AI-powered applications. We are one of the strongest concentrations of neurosymbolic AI talent in industry. Our team includes original contributors to the Lean theorem prover and is advised by Lean's Chief Architect. We bring together researchers and engineers from both the AI and formal methods communities, a combination that is extraordinarily rare and increasingly essential. We build on Amazon's 10+ year track record of bringing automated reasoning to production at scale. AWS pioneered the use of formal methods in cloud infrastructure, from network reachability analysis to cryptographic protocol verification to access policy reasoning, systems that serve hundreds of millions of customers today. Now we're taking the next giant leap: fusing that heritage with frontier AI to make every AI system verifiable, trustworthy, and safe.

Senior ML Kernel Performance Engineer Toronto, Ontario, CAN The Annapurna Labs team at Amazon builds Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium. The Acceleration Kernel Library team is at the forefront of maximizing performance for Amazon's custom ML accelerators. Working at the hardware-software boundary, our engineers craft high-performance kernels for ML functions, ensuring every FLOP counts in delivering optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. The Amazon Neuron SDK, developed by the Annapurna Labs team at Amazon, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Inferentia and Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch, enabling unparalleled ML inference and training performance. As part of the broader Neuron Compiler organization, our team works across multiple technology layers - from frameworks and compilers to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to work at the intersection of machine learning, high-performance computing, and distributed architectures, where you'll help shape the future of AI acceleration technology This is an opportunity to work on cutting-edge products at the intersection of machine-learning, high-performance computing, and distributed

Applied Scientist II - AMZ9890756 San Diego, California, USA MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Applied Scientist II Job Location: San Diego, California Job Number: AMZ9890756 Position Responsibilities: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. 40 hours / week, 8:00am-5:00pm, Salary Range: $142,800/year to $193,200/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. Amazon.com is an Equal Opportunity-Affirmative Action Employer - Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000 Basic Qualifications: Position Requirements: Master's degree or foreign equivalent degree in Computer Science, Machine Learning, Statistics, or a related field and one year of research or work experience in the job offered or as a Research Scientist, Research Assistant, Software Engineer, or a related occupation. Employer will accept a Bachelor's degree or foreign equivalent degree in Computer Science, Machine Learning, Statistics, or a related

Sr. Applied Scientist, SSG Science Bengaluru, Karnataka, IND Amazon Devices is an inventive research and development company that designs and engineer high-profile devices like the Kindle family of products, Fire Tablets, Fire TV, Health Wellness, Amazon Echo & Astro products. This is an exciting opportunity to join Amazon in developing state-of-the-art techniques that bring Gen AI on edge for our consumer products. We are looking for exceptional scientists to join our Applied Science team and help develop the next generation of edge models, and optimize them while doing co-designed with custom ML HW based on a revolutionary architecture. Work hard. Have Fun. Make History. Key job responsibilities What will you do? - Quantize, prune, distill, finetune Gen AI models to optimize for edge platforms - Fundamentally understand Amazon's underlying Neural Edge Engine to invent optimization techniques - Analyze deep learning workloads and provide guidance to map them to Amazon's Neural Edge Engine - Use first principles of Information Theory, Scientific Computing, Deep Learning Theory, Non Equilibrium Thermodynamics - Train custom Gen AI models that beat SOTA and paves path for developing production models - Collaborate closely with compiler engineers, fellow Applied Scientists, Hardware Architects and product teams to build the best ML-centric solutions for our devices - Publish in open source and present on Amazon's behalf at key ML conferences - NeurIPS, ICLR, MLSys. Basic Qualifications: - 3+ years of building machine learning models for business application experience - PhD, or Master's degree and 6+ years of applied research experience

Machine Learning - Compiler Engineer II, Annapurna Labs Seattle, Washington, USA The Product: AWS Machine Learning accelerators are at the forefront of AWS innovation and one of several AWS tools used for building Generative AI on AWS. The Inferentia chip delivers best-in-class ML inference performance at the lowest cost in cloud. Trainium will deliver the best-in-class ML training performance with the most teraflops (TFLOPS) of compute power for ML in the cloud. This is all enabled by cutting edge software stack, the AWS Neuron Software Development Kit (SDK), which includes an ML compiler, runtime and natively integrates into popular ML frameworks, such as PyTorch, TensorFlow and MxNet. AWS Neuron and Inferentia are used at scale with customers like Snap, Autodesk, Amazon Alexa, Amazon Rekognition and more customers in various other segments. The Team: As a whole, the Amazon Annapurna Labs team is responsible for silicon development at AWS. The team covers multiple disciplines including silicon engineering, hardware design and verification, software and operations. The AWS Neuron team works to optimize the performance of complex neural net models on our custom-built AWS hardware. More specifically, the AWS Neuron team is developing a deep learning compiler stack that takes neural network descriptions created in frameworks such as TensorFlow, PyTorch, and MXNET, and converts them into code suitable for execution. As you might expect, the team is comprised of some of the brightest minds in the engineering, research, and product communities, focused on the ambitious goal of creating a toolchain that will provide