Production ML Engineer Lead - Pipelines & Inference Scale

About the role
Our client is a well-funded AI startup building production-grade ML infrastructure used by enterprise customers. They are looking for a Senior AI/ML Engineer to own model training pipelines, evaluation systems, and inference serving l-time, on-site in San Francisco.

• Design and ship end-to-end ML systems: data pipelines, training, evaluation, deployment

• Own model performance, latency, and cost trade-offs in production

• Build evaluation harnesses and offline benchmarks for fast iteration

• Work directly with product to translate ambiguous goals into measurable model improvements

• Mentor other engineers on ML best practices and code quality

• 4+ years of applied ML engineering in production environments

• Hands-on experience with LLMs, fine-tuning, RAG, or large-scale recommender systems

• Strong Python and PyTorch (or JAX) fundamentals

• Experience with distributed training, GPU optimization, or inference serving

• Pragmatic about trade-offs between research-grade and ship-grade work

This role is presented by a recruiting partner. Company name shared after an initial conversation.