Neural Magic jobs

Neuroscience Specialist - Freelance AI Trainer Project United States of America Are you a neuroscience expert eager to shape the future of AI? Large‑scale language models are evolving from clever chatbots into powerful engines of scientific discovery. With high‑quality training data, tomorrow's AI can democratize world‑class education, keep pace with cutting‑edge research, and streamline lab work for scientists everywhere. That training data begins with you-we need your expertise to help power the next generation of AI. We're looking for neuroscience specialists who live and breathe neuroanatomy, neurophysiology, neurochemistry, cognitive neuroscience, behavioral neuroscience, neuroimaging, neurodegenerative diseases, neural networks, sensory processing, and synaptic plasticity. You'll challenge advanced language models on topics like brain structure and function, neuroplasticity, neurogenesis, neurotransmitter systems, brain disorders, and neurodevelopmental processes-documenting every failure mode so we can harden model reasoning. On a typical day, you will converse with the model on lab scenarios and theoretical neuroscience questions, verify factual accuracy and logical soundness, capture reproducible error traces, and suggest improvements to our prompt engineering and evaluation metrics. A master's or PhDs in neuroscience or a closely related life‑science field is ideal; peer‑reviewed publications, wet‑lab or field research, or hands‑on neuroimaging projects signal fit. Clear, metacognitive communication-“showing your work”-is essential. Ready to turn your neuroscience expertise into the knowledge base for tomorrow's AI? Apply today and start teaching the model that will teach the world. We offer a pay range of $6-to- $65 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location. Final

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

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 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, 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

Telco Consultant Tokyo posted: Posted 30+ Days Ago

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

Senior Agile Practitioner Raleigh posted: Posted 30+ Days Ago

North America Public Sector (NAPS) Customer Success Executive - Intelligence Community Remote US DC posted: Posted 4 Days Ago

Junior Solution Architect Sao Paulo posted: Posted Yesterday

Principal Software Engineer - AI Experiment Tracking (Ireland) 4 Locations posted: Posted 3 Days Ago

Agile Development Coach Tokyo posted: Posted 2 Days Ago

Machine Learning Engineer, Distributed vLLM Boston posted: Posted 16 Days Ago

Strategic Account manager Mumbai posted: Posted 10 Days Ago

Staff Python / PyTorch Developer — Frontend Inference Compiler – Dubai Europe; Remote, California, United States; UAE Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. Cerebras' current customers include top model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras , to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. About the Role: Would you like to participate in creating the fastest Generative Models inference in the world? Join the Cerebras Inference Team to participate in development of unique Software and Hardware combination that sports best inference characteristics in the market while running largest models available. Cerebras wafer scale inference platform allows running Generative models with unprecedented speed thanks to unique hardware architecture that provides fastest access to local memory, ultra-fast interconnect and huge amount

Account Executive - Banking Istanbul - MSO posted: Posted Yesterday

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

Ansible Consultant Brasilia - MSO posted: Posted 9 Days Ago

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.

Senior Project Manager Canberra 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

Consultant, Ansible 4 Locations posted: Posted 4 Days Ago