Machine Learning Engineer
About the Role
This is a mid-level Machine Learning Engineer role at a small, fast-moving AI startup in the recruiting and talent-matching space. You will own the full ML lifecycle, from problem definition through production monitoring, working closely with product and engineering to ship models that directly drive business outcomes.
What You'll Do
Design, train, and evaluate machine learning models for production use cases.
Build end-to-end ML pipelines covering data preprocessing, model serving, and monitoring.
Partner with product and engineering teams to translate business requirements into ML solutions.
Debug and optimize model performance in production, iterating based on real-world feedback.
Write clean, maintainable code and contribute to ML infrastructure and tooling.
Participate in code reviews and share knowledge across the team.
What We're Looking For
3 to 7 years of professional experience in machine learning or software engineering, with substantive hands-on applied ML work in production systems.
Demonstrated experience working in a startup or similarly fast-paced, resource-constrained environment with rapid iteration cycles.
Strong fundamentals in ML: model selection, evaluation, feature engineering, and validation.
Proficiency in Python and common ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
Proven ability to build, deploy, and maintain ML systems at scale, not just academic or prototype work.
Experience with data pipelines, feature engineering, or model evaluation in production contexts.
Familiarity with cloud ML platforms or MLOps tooling (such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker) is a plus.
Experience with A/B testing, experimentation frameworks, or production model monitoring is a plus.
Comfort with ambiguity and a strong ability to prioritize impact in a dynamic setting.
Location
Based in San Francisco, California. Please confirm the specific work arrangement directly with the team.