5 months ago
Responsibilities
- Design and ship end-to-end ML systems, including data pipelines, training, evaluation, and deployment.
- Own model performance, latency, and cost trade-offs in production.
- Build evaluation harnesses and offline benchmarks for rapid iteration.
- Partner with product teams to translate ambiguous goals into measurable model improvements.
- Mentor engineers on ML best practices and code quality.
Requirements
- 4+ years of applied ML engineering experience 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 judgment regarding trade-offs between research-grade and production-ready work.
Benefits
- Full-time, on-site position in San Francisco.