4 hours ago
San Francisco, CA, USAMid Level / Senior
Responsibilities
- Design, train, and evaluate machine learning models for production use cases.
- Build end-to-end ML pipelines for data preprocessing, model serving, and monitoring.
- Translate business requirements into machine learning solutions with product and engineering teams.
- Debug and optimize model performance in production using real-world feedback.
- Write maintainable code and contribute to ML infrastructure and tooling.
- Participate in code reviews and share knowledge across the team.
Requirements
- 3–7 years of professional experience in machine learning or software engineering with hands-on applied ML work in production systems.
- Experience working in a startup or similarly fast-paced, resource-constrained environment.
- Strong fundamentals in model selection, evaluation, feature engineering, and validation.
- Proficiency in Python and common ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Experience building, deploying, and maintaining ML systems at scale rather than only academic or prototype systems.
- 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 preferred.
- Experience with A/B testing, experimentation frameworks, or production model monitoring is preferred.
- Ability to prioritize impact and work comfortably with ambiguity in a dynamic environment.
Benefits
- Based in San Francisco, California; specific work arrangement should be confirmed directly with the team.