As a Senior Research Engineer embedded in our science team, you'll be instrumental in transforming innovative research into high-performance production systems. You'll collaborate directly with scientists to build and optimize large-scale transformer models for robotics applications. In this role, you'll balance deep technical optimization work with strategic input on model architecture decisions, ensuring our innovative robotics models are designed with performance in mind from the ground up. You'll leverage PyTorch and NVIDIA's acceleration stack and other compilation techniques to tackle ambitious performance targets, working at the intersection of large language models and real-world robotics applications. Key job responsibilities - Design, implement, and optimize distributed training systems that scale across thousands of GPUs and nodes for large-scale training workloads. - Develop high-performance optimizations to maximize throughput and efficiency. - Develop reusable frameworks and libraries to improve training reproducibility, reliability, and scalability for new model architectures. - Establish standards for reliability, maintainability, and security, ensuring systems are robust under rapid iteration. - Collaborate with researchers to influence model architectures for optimal hardware utilization - Develop comprehensive benchmarking frameworks to measure and optimize model performance A day in the life In this role, you will: - Optimize transformer blocks using custom CUDA kernels and TensorRT optimization techniques - Partner with scientists to analyze model architectures and propose efficiency improvements - Implement and benchmark various optimization strategies for large-scale models - Debug performance bottlenecks using NVIDIA profiling tools - Participate in technical discussions about new model architectures with the science team - Manage pre/post training runs and continue improve system stability and throughput - Prototype new acceleration approaches using emerging compilation frameworks BASIC QUALIFICATIONS - 5+ years of non-internship professional software development experience, or Bachelor's degree in computer science or equivalent - 5+ years of programming with at least one software programming language experience - 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience - Experience as a mentor, tech lead or leading an engineering team - Bachelor's degree in computer science or equivalent - Expertise in Python, PyTorch, and CUDA programming - Experience with TensorRT or similar ML optimization frameworks - Ability to optimize ML models for production - Experience working directly with research teams - Experience working on distributed training for the foundation model to make them stable, reliable, and performant PREFERRED QUALIFICATIONS - 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience - Track record of improving research productivity through infrastructure design or process improvements. - Experience in ML infrastructure such as Pytorch, Megatron, TorchTitan, etc - Experience with ML compilers (ONNX Runtime, TVM, etc.) - Experience with transformer model optimization or RL framework development - Background in performance profiling and optimization - Track record of building robust monitoring systems - Experience with large-scale ML serving systems Our compensation reflects the cost of labor across several U.S. geographic markets. The base pay for this position ranges from $150,000/year in our lowest geographic market up to $300,000/year in our highest geographic market. Pay is based on a number of factors, including market location, and may vary depending on job-related knowledge, skills, and experience. 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, please visit https://www.aboutamazon.com/workplace/employee-benefits. Applicants should apply via our internal or external career site.