Senior ML Ops Engineer
Skills
- 5+ years owning production ML serving systems or related platform/infrastructure engineering experience.
- Hands-on experience deploying and maintaining LLMs and deep learning models in production.
- Strong Python and software engineering fundamentals with infrastructure familiarity.
- Experience with cloud platforms (AWS, GCP, or Azure) and ML lifecycle tooling (model registries, experiment tracking).
- Practical knowledge of inference optimization: batching, memory management, quantization, CPU/GPU interaction.
- Experience with CI/CD, testing, observability, and incident response for ML systems.
- Ability to reason about tradeoffs between latency, cost, throughput, and reliability.
Responsibilities
- Build, maintain, and optimize production-grade ML pipelines to move models from experimentation to production reliably.
- Define and implement model versioning, rollout, rollback, and lifecycle management practices.
- Set and enforce serving-layer SLAs (latency, availability, GPU utilization, TTFT, ITL) and build observability and alerting.
- Apply software engineering best practices (testing, CI/CD, reproducibility) to ML workflows.
- Ensure ML systems are secure, cost-efficient, and scalable in partnership with DevOps while owning ML-specific operational needs.
- Collaborate cross-functionally with ML, Data, Product, and DevOps teams to translate requirements into production-ready systems and influence roadmap.
Education
- Bachelor’s or Master’s degree in Computer Science, Data Science, or related field, or equivalent practical experience.
Benefits
- Competitive base salary ($200,000–$250,000) with equity (stock options).
- Medical, dental, and vision coverage.
- 401(k) plan.
- Flexible PTO and company holidays.
- Fully remote work within the United States with periodic company offsites and team gatherings.
Average salary estimate
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