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Mid-Level Machine Learning EngineerSierracorp • San Francisco, California, United States
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XX

Mid-Level Machine Learning Engineer

Sierracorp
  • US
    San Francisco, California, United States
  • US
    San Francisco, California, United States
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About

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.
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  • San Francisco, California, United States

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  • English
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