Mid-Level Machine Learning Engineer

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

Own the full ML lifecycle—from prototype to production—at a company that moves fast. You will operate at the intersection of research and product, independently owning an ML problem end to end, balancing research quality with engineering discipline. You will work closely with cross‑functional teams and play a key role in mentoring junior engineers.

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, embeddings, and Retrieval‑Augmented Generation (RAG).
• Design and execute rigorous experiments (A/B tests) and communicate impact to stakeholders.
• Set up observability for deployed models, monitoring for data drift, model degradation, and latency.
• Review code from peers and junior engineers, and contribute to roadmap planning and technical decision‑making.

Requirements

Key Focus: Own full ML lifecycle and lead experiments.

Required Skills:

• 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.
• Experience deploying and serving models at scale on cloud infrastructure (SageMaker, Vertex AI, Azure ML, or equivalent).
• Strong data engineering skills (SQL, Spark or Dask, dbt, Airflow or Prefect).
• Familiarity with LLMs and the modern generative AI stack.
• Solid software engineering habits: unit testing, version control, containerization (Docker), and CI/CD pipelines.

Valuable Experience (Nice to Have):

• Experience with real‑time inference, streaming ML, or multi‑modal models.
• Background in NLP, recommendation systems, or forecasting at scale.
• Prior experience hiring or technically mentoring junior ML engineers.