Sr. Machine Learning Engineer, PXT EXR-Ops Tech
Amazon · Bellevue, Washington, USA ·
- Seniority
- Senior
- Category
- ML ai
We are looking for a Senior Machine Learning Engineer who will serve as a technical leader, driving the design and delivery of complex, large-scale ML systems that have significant business impact. In this role, you will define the ML architecture and technical strategy for your team, influence cross-organizational ML platform decisions, and raise the bar for ML engineering excellence. You will build super-intelligent AI systems, lead the development of production-grade AI services including generative AI and LLMs, and mentor a team of ML engineers. This is a high-impact role requiring deep technical expertise, strong judgment, and the ability to operate in ambiguous problem spaces.
Key job responsibilities
• Define and drive the ML technical strategy and architecture for your team and adjacent teams
• Design and lead the implementation of complex, multi-model ML systems at Amazon scale
• Architect ML platforms and frameworks that enable rapid experimentation and deployment across the organization
• Lead the design of real-time and batch ML serving infrastructure with high availability and low latency
• Drive adoption of ML best practices including MLOps, model governance, responsible AI, and cost optimization
• Evaluate and introduce new ML technologies, frameworks, and methodologies (e.g., foundation models, RAG, fine-tuning, RLHF)
• Lead cross-functional initiatives spanning science, engineering, product, and business teams
• Influence org-wide technical decisions through design reviews, architecture reviews, and technical writing (design documents, white papers)
• Mentor and develop L4 and L5 ML engineers; raise the hiring bar as a bar raiser or interviewer
• Drive operational excellence: define SLAs, build dashboards, automate incident response, and reduce operational burden
Key job responsibilities
• Define and drive the ML technical strategy and architecture for your team and adjacent teams
• Design and lead the implementation of complex, multi-model ML systems at Amazon scale
• Architect ML platforms and frameworks that enable rapid experimentation and deployment across the organization
• Lead the design of real-time and batch ML serving infrastructure with high availability and low latency
• Drive adoption of ML best practices including MLOps, model governance, responsible AI, and cost optimization
• Evaluate and introduce new ML technologies, frameworks, and methodologies (e.g., foundation models, RAG, fine-tuning, RLHF)
• Lead cross-functional initiatives spanning science, engineering, product, and business teams
• Influence org-wide technical decisions through design reviews, architecture reviews, and technical writing (design documents, white papers)
• Mentor and develop L4 and L5 ML engineers; raise the hiring bar as a bar raiser or interviewer
• Drive operational excellence: define SLAs, build dashboards, automate incident response, and reduce operational burden