AI role · Open offer
Senior ML Engineer — cloud AI services
Amazon Web Services
This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.
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At a glance
How you'll work, where, and key listing dates.
- Employment
- Full-time
- Workplace
- Hybrid
- Location
- 21515 Ridgetop Cir, Sterling, VA 20166, United States · Hybrid
- Compensation
- $185k–$260k/year + RSUs
- Experience level
- Senior
- Team
- SageMaker AI
- Benefits & perks
About this role
What the team is looking for and how this position fits.
Amazon Web Services is hiring a Senior Senior ML Engineer — cloud AI services on the SageMaker AI team (Hybrid · Ashburn, United States). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.
You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.
**Amazon Web Services** operates the world's largest cloud — **SageMaker**, **Bedrock**, and AI services engineered from **Northern Virginia** (Ashburn/Sterling corridor).
Senior ML Engineer (hybrid NoVA): **foundation model hosting**, **MLOps**, and **customer-facing AI** APIs at hyperscale.
About AWS
- Ashburn region is the densest cloud infrastructure hub globally.
- Teams build services used by millions of AWS customers.
- Leadership principles drive operational excellence.
What you will do
- Improve SageMaker/Bedrock latency and cost efficiency.
- Design multi-tenant inference with isolation guarantees.
- Partner with solutions architects on enterprise rollouts.
Stack & environment
- Python, PyTorch, LangChain, AWS, Kubernetes.
- Hybrid Sterling/Ashburn; US citizenship not required for most roles.
What we look for
- 5+ years ML platform or distributed systems.
- Authorized to work in the United States.
Skills we're hiring for
What each capability means for this role—not a keyword checklist.
- Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
- PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
- SQL
Tools & stack
Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).
- AWSTool
Regular use of AWS in the LLM & generative AI stack—configuration, debugging, and team conventions.
- KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
You're a strong fit if…
Tailored to this listing—use it as a self-check before you apply.
- ✓You can show evidence in your application: MLOps, foundation model hosting, or Bedrock integrations.
- ✓Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, SQL, LangChain, and AWS.
- ✓You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
- ✓You are comfortable with the hybrid rhythm (Hybrid · Ashburn, United States)—onsite collaboration when it matters, deep work when remote.
- ✓You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
- ✓
Apply
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What the employer asked for
MLOps, foundation model hosting, or Bedrock integrations.
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