Senior Machine Learning Engineer

MetAntz; San Francisco (CA); On-site; USD 150,000 - 200,000; Full time

Job summary

An AI-focused technology company in San Francisco is seeking a Senior Machine Learning Engineer to take ownership of the full ML lifecycle. You will design, build, and operate ML systems that directly impact the product, emphasizing reliability and cost-efficiency. Aimed at solving real business problems with a focus on applied AI, candidates should have over 7 years of experience, technical leadership skills, and proficiency in ML technologies including PyTorch and TensorFlow. This role requires strong accountability and the ability to debug complex production scenarios.

Qualifications

• 7+ years of hands-on ML engineering with clear senior-level ownership.
• Experience in building production-grade ML systems.

Responsibilities

• Manage the end-to-end ML lifecycle for real products.
• Build production-grade ML systems focusing on latency and reliability.
• Implement MLOps foundations and deploy automated testing.
• Lead technical discussions, conduct code reviews, and mentor.
• Take ownership of debugging production failures.

Skills

Machine Learning Engineering

PyTorch

TensorFlow

MLOps

Technical Leadership

Job description

Role Context

Nenu AI builds applied AI systems that must survive real users real data and real constraints. This role is not research theater. It is production ownership. You will design build deploy and operate ML systems that directly shape the product. Expect ambiguity. Expect velocity. Expect accountability.

What You Will Own

• End-to-end ML lifecycle across real products. Data ingestion feature design model selection training deployment monitoring iteration. No handoffs.
• Production-grade ML systems built with PyTorch or TensorFlow with attention to latency reliability cost and failure modes.
• Applied GenAI and LLM work where it creates measurable value. Fine-tuning RAG prompt orchestration evaluation and guardrails. No hype-first work.
• MLOps foundations. Model versioning CI,CD automated testing deployment pipelines serving layers monitoring and A,B experimentation.
• Tight partnership with product engineering and data. Translate fuzzy business problems into tractable ML solutions and quantify impact.
• Technical leadership. Code reviews model reviews mentoring and raising the bar for ML engineering discipline.
• Incident ownership. Debug production failures data drift performance regressions and bias issues calmly and decisively.

Required Profile

7+ years of hands-on ML engineering with clear senior-level ownership of