End-to-End ML Engineer — Production Pipelines & Mentoring

Sierracorp; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Sierracorp is seeking an experienced ML Engineer in San Francisco to own the full ML lifecycle—from prototype to production. You will design and maintain production-grade ML pipelines, apply modern techniques, and mentor junior engineers. The ideal candidate has over 5 years of experience, deep proficiency in Python along with MLOps and data engineering skills. This role offers a chance to work at the intersection of research and product, contributing significantly to advanced ML projects.

Qualifications

• 5+ years of professional ML engineering experience with demonstrated production deployments.
• Deep proficiency in Python and ML frameworks (PyTorch, TensorFlow/Keras, scikit‑learn).
• Hands-on experience with MLOps tooling: experiment tracking, model registry, feature stores, and CI/CD for ML.

Responsibilities

• Own end‑to‑end ML projects: problem framing, data strategy, model development, deployment, and monitoring.
• Design and maintain production‑grade ML pipelines with a focus on reliability, scalability, and reproducibility.
• Pragmatically apply modern techniques, including fine‑tuned LLMs and Retrieval‑Augmented Generation.

Skills

Machine Learning engineering experience

Python proficiency

ML frameworks (PyTorch, TensorFlow/Keras)

MLOps tooling

Data engineering skills (SQL, Spark, dbt)

Familiarity with LLMs

Job description

Sierracorp is seeking an experienced ML Engineer in San Francisco to own the full ML lifecycle—from prototype to production. You will design and maintain production-grade ML pipelines, apply modern techniques, and mentor junior engineers. The ideal candidate has over 5 years of experience, deep proficiency in Python along with MLOps and data engineering skills. This role offers a chance to work at the intersection of research and product, contributing significantly to advanced ML projects.