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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

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

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

Senior Full Stack JavaScript AI Developer Nanakramguda, Hyderabad, India posted: Posted 30+ Days Ago

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

Machine Learning - Compiler Engineer , AWS Neuron, Annapurna Labs Cupertino, California, USA Do you want to be part of AI revolution? At AWS our vision is to make deep learning pervasive for everyday developers and to democratize access to AI hardware and software infrastructure. In order to deliver on that vision, we've created innovative software and hardware solutions that make it possible. AWS Neuron is the SDK that optimizes the performance of complex ML models executed on AWS Inferentia and Trainium, our custom chips designed to accelerate deep-learning workloads. This role is for a software engineer in the Compiler team for AWS Neuron. As part of this role, you will be responsible for building next generation Neuron compiler which transforms ML models written in ML frameworks (e.g, PyTorch, TensorFlow, and JAX) to be deployed AWS Inferentia and Trainium based servers in the Amazon cloud. You will be responsible for solving hard compiler optimization problems to achieve optimum performance for variety of ML model families including massive scale large language models like Llama, Deepseek, and beyond as well as stable diffusion, vision transformers and multi-model models. You will be required to understand how these models work inside-out to make informed decisions on how to best coax the compiler to generate optimal implementation instruction. You will leverage your technical communications skill to partner with internal and external customers/stakeholders and will be involved in pre-silicon design, bringing new products/features to market, ultimately, making Neuron compiler highly performant and easy-to-use. Experience in object-oriented

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

Machine Learning Performance Engineer, Annapurna Labs Tel Aviv-Yafo, Tel Aviv, ISR Our team is responsible for the AWS Neuron software stack, which powers Generative AI and other advanced ML workloads on AWS's custom-built ML accelerators - Inferentia and Trainium. These accelerators deliver best-in-class performance and cost-efficiency for ML inference and training in the cloud. We're building a new core group of engineers in TLV (Tel Aviv) to drive innovation in ML systems performance and software. As a Machine Learning Performance Engineer, you'll help shape the direction of the team from the ground up and work on: Optimizing system performance across the entire ML software stack Analyzing high-performance ML workloads running on Annapurna hardware Developing high-performance kernels for critical ML operations Enhancing the Neuron SDK to improve developer experience and system capabilities Collaborating across Compiler, Frameworks, and Hardware teams to maximize end-to-end performance As part of the Performance Engineering Team, you'll contribute to projects involving instruction scheduling, memory management, parallelism, kernel optimization, and compiler enhancements to maximize end-to-end performance. This is a unique opportunity to be at the intersection of ML and systems within AWS, helping to build the future of AI infrastructure - right here in Tel Aviv. Key job responsibilities Our engineers collaborate across diverse teams, projects, and environments to have a firsthand impact on our global customer base. You will: Solve challenging technical problems, often ones not solved before, at every layer of the stack. Design, implement, test, deploy and maintain innovative software solutions to transform service performance,

Software Engineering Manager, ML Kernel Performance, 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

SDE-II, AI Core Infra - AI Analytics and Insights Bengaluru, Karnataka, IND Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising solutions that drive product discovery and sales. We deliver billions of ad impressions every single day on behalf of our advertisers and generate massive volumes of data across the advertising lifecycle. We are building a conversational AI assistant that empowers data engineers, analysts, and business stakeholders to interact with our vast advertising data lake through natural language. Using state-of-the-art generative AI, SpektrBot enables teams to generate insights faster by translating natural language queries into accurate, trusted data retrievals, eliminating the friction between questions and answers. As an SDE II on this team, you will be at the center of designing, building, and scaling the systems that power this conversational AI experience. You will architect robust backend services, build reliable integrations with large language models (LLMs), develop retrieval-augmented generation (RAG) pipelines, and engineer the feedback loops that allow our system to continuously evaluate and improve itself. You will work alongside applied scientists, product managers, and fellow engineers in a fast-paced, high-ownership environment where your contributions directly shape the product and influence how thousands of internal customers interact with data. This is a rare opportunity to build a 0-to-1 product at Amazon's scale, one that sits at the intersection of generative AI, data engineering, and conversational interfaces. If you are passionate about building production-grade AI-powered systems, thrive in ambiguity,

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. 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

Sr. Software Engineer- AI/ML, AWS Neuron Distributed Training - Performance Optimization Seattle, Washington, USA Annapurna Labs designs silicon and software that accelerates innovation. Customers choose us to create cloud solutions that solve challenges that were unimaginable a short time ago-even yesterday. Our custom chips, accelerators, and software stacks enable us to take on technical challenges that have never been seen before, and deliver results that help our customers change the world. AWS Neuron is the complete software stack for the AWS Trainium and Inferentia cloud-scale machine learning accelerators and the Trn3/Trn2/Trn1 and Inf2/Inf1 servers that use them. This role is for a software engineer in the Distributed Training team for AWS Neuron. This role is responsible for development, enablement and performance tuning of a wide variety of ML model families, including massive scale multi-modal large language models like Llama, Qwen, gpt-oss, DeepSeek and beyond, as well as multi-modal generation models such as Stable Diffusion, Flux, WAN, and many more. The Distributed Training team works side by side with chip architects, compiler engineers and runtime engineers to create, build and tune distributed training solutions with AWS Trainium, maximize training throughput, minimize time-to-convergence, and push the boundaries of training efficiency on Trainium. You will identify and resolve performance bottlenecks across the stack, from collective communications and memory utilization to compiler optimizations and kernel performance. Key job responsibilities This role will lead efforts to optimize distributed training performance on Trainium, with a primary focus on maximizing training throughput, model flops utilization, and efficiency

Senior AI/ML Applied Scientist - Generative AI Country USA | Internal Posting Location Palo Alto We help the world run better At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging - but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed. Summary: We are searching for an experienced Sr AI/ML Applied Scientist to join our newly formed ABAP Foundation Model team in New Port Beach. This dynamic team is at the forefront of SAP's cloud transformation efforts, leveraging large language models to accelerate our customers' data and code transformation. The team collaborates closely with established teams in Europe and India, various SAP stakeholders, UC Irvine, and early adopter customers. You will play a key role in shaping our research collaboration with UC Irvine and coordinating with data science leads of ABAP Foundation Model teams globally. What you'll do: Model Development: Oversee the development, fine-tuning, and integration of foundation models into SAP solutions to streamline cloud transformation journeys. Collaboration: Engage with stakeholders in different teams at SAP as well as early adopter customers and research collaborations. Leadership: Mentor and lead junior and senior data scientists,

Associate Machine Learning Engineer India - Hyderabad posted: Posted 30+ Days Ago

Associate Machine Learning Engineer India - Hyderabad posted: Posted 7 Days Ago

Process Development Senior Scientist - Synthetic Peptides, Oligonucleotides, ADCs US - California - Thousand Oaks posted: Posted 14 Days Ago

Software Engineer- AI/ML, AWS Neuron Distributed Training - Performance Optimization Seattle, Washington, USA Annapurna Labs designs silicon and software that accelerates innovation. Customers choose us to create cloud solutions that solve challenges that were unimaginable a short time ago-even yesterday. Our custom chips, accelerators, and software stacks enable us to take on technical challenges that have never been seen before, and deliver results that help our customers change the world. AWS Neuron is the complete software stack for the AWS Trainium and Inferentia cloud-scale machine learning accelerators and the Trn3/Trn2/Trn1 and Inf2/Inf1 servers that use them. This role is for a software engineer in the Distributed Training team for AWS Neuron. This role is responsible for development, enablement and performance tuning of a wide variety of ML model families, including massive scale multi-modal large language models like Llama, Qwen, gpt-oss, DeepSeek and beyond, as well as multi-modal generation models such as Stable Diffusion, Flux, WAN, and many more. The Distributed Training team works side by side with chip architects, compiler engineers and runtime engineers to create, build and tune distributed training solutions with AWS Trainium, maximize training throughput, minimize time-to-convergence, and push the boundaries of training efficiency on Trainium. You will identify and resolve performance bottlenecks across the stack, from collective communications and memory utilization to compiler optimizations and kernel performance. Key job responsibilities This role will help lead efforts to optimize distributed training performance on Trainium, with a primary focus on maximizing training throughput, model flops utilization, and efficiency

Sr Machine Learning Engineer India - Hyderabad posted: Posted 30+ Days Ago

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

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

Sr. Software Engineer- AI/ML, AWS Neuron Apps Seattle, Washington, USA Shape the Future of AI Accelerators at AWS Neuron Join the elite team behind AWS Neuron-the software stack powering AWS's next-generation AI accelerators Inferentia and Trainium. As a Senior Software Engineer in our Machine Learning Applications team, you'll be at the forefront of deploying and optimizing some of the world's most sophisticated AI models at unprecedented scale. What You'll Impact: • Pioneer distributed inference solutions for industry-leading LLMs such as GPT, Llama, Qwen • Optimize breakthrough language and vision generative AI models • Collaborate directly with silicon architects and compiler teams to push the boundaries of AI acceleration • Drive performance benchmarking and tuning that directly impacts millions of inference calls globally Key job responsibilities You will drive the Evolution of Distributed AI at AWS Neuron As a Technical Leader at the forefront of AWS's AI Accelerator, you'll architect the bridge between ML frameworks including PyTorch, JAX and AI hardware. This isn't just about just optimization-it's about revolutionizing how AI models run at scale. Technical Impact You'll Drive: • Spearhead distributed inference architecture for PyTorch and JAX using XLA • Engineer breakthrough performance optimizations for AWS Trainium and Inferentia • Develop ML tools to enhance LLM accuracy and efficiency • Transform complex tensor operations into highly optimized hardware implementations • Pioneer benchmarking methodologies that shape next-gen AI accelerator design What Makes This Role Unique: • Direct influence on AWS's AI infrastructure used by thousands of ML applications • Full-stack optimization

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

ML Compiler Engineer II - Neuron Kernel Interface , Annapurna Labs Seattle, Washington, USA The AWS Neuron Compiler team is actively seeking skilled compiler engineers to join our efforts in developing a 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, TensorFlow, and JAX. Your role will involve working closely with our custom-built Machine Learning accelerators, including Inferentia/Trainium, which represent the forefront of AWS innovation for advanced ML capabilities, powering solutions like Generative AI. Key job responsibilities As a ML Compiler Engineer II on the Neuron Compiler Automated Reasoning Group, you will develop and maintain tooling for best-in-class technology for raising the bar of the Neuron Compiler's accuracy and reliability. You will help lead the efforts building fuzzers and specification synthesis tooling for our LLVM-based compiler. You will work in a team with a science focus, and strive to push what we do to the edge of what is known, to best deliver our customers. Strong software development skills using C++/Python are critical to this role. A science background in compiler development is strongly preferred. A background in Machine Learning and AI accelerators is preferred, but not required. In order to be considered for this role, candidates must be currently located or willing to relocate to Seattle (Preferred), Cupertino, Austin, or Toronto. Basic Qualifications: - 3+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing