Amazon.com Services LLC
Software Development, subsidiaries
SeniorMLEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior ML Engineer at Amazon.com Services LLC. Skills: ML systems, Robot locomotion, Robot perception, Robot manipulation, Robot navigation, Human-robot interaction, ML training, ML deployment. Design ML training infrastructure. Build ML training infrastructure”
Industry & Context.
What They're Looking For.
Must Have
5+ years software development, 5+ years programming, 5+ years system design, Experience as mentor, Experience leading engineering team, Bachelor's degree or above, Machine Learning fundamentals, Large Language Model fundamentals, ML training/inference lifecycles, Model execution optimization, Experience with ML tools, Experience with ML methods, Experience with Kubernetes, Experience with Docker, Experience with containers ecosystem, Experience with programming/scripting
Nice to Have
Building cloud-based architecture, Operating cloud-based architecture, Robotics data experience, Real-time inference systems experience, Model optimization techniques familiarity, Reinforcement learning experience, Simulation-based training pipelines experience
What You'll Do.
Design ML training infrastructure
Build ML training infrastructure
Maintain ML training infrastructure
Design training pipelines
Build training pipelines
Develop experiment tracking systems
Develop model versioning systems
Develop reproducibility systems
Build deployment infrastructure
Optimize model inference
Accelerate path to production
Contribute to data pipelines
Contribute to labeling infrastructure
How You'll Work.
Team & Collaboration
Research teams; Data platform team
Full Job Description
We are seeking a Senior ML Engineer to build and scale the machine learning systems that power our intelligent robots. In this role, you will design and maintain the infrastructure for training, evaluating, and deploying the ML models that enable robot locomotion, perception, manipulation, navigation, and human-robot interaction. You'll work at the intersection of machine learning and systems engineering, ensuring our ML training and deployment systems are robust, efficient, and scalable as we grow from prototype to production. Key job responsibilities - Design and build scalable ML training infrastructure, including distributed training pipelines and GPU cluster management both in the cloud and on-prem - Develop systems for experiment tracking, model versioning, and reproducibility - Build deployment infrastructure for serving ML models on robotic hardware with strict latency requirements - Optimize model inference for edge devices and embedded systems - Collaborate with research teams to accelerate the path from experimentation to production - Contribute to data pipelines and labeling infrastructure as needed, in partnership with the data platform team Basic Qualifications: - 5+ years of non-internship professional software development experience - 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 or above in computer science, machine learning, engineering, or related fields, or Master's degree - Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience in development in the last 3 years - Experience with machine learning (ML) tools and methods - Experience in Kubernetes, Docker or containers ecosystem,
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