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About the Role
Build and deploy ML systems that power a core product in a fast-moving startup environment. Work across the full ML lifecycle from problem definition through production monitoring, collaborating with product and engineering teams to ship models that drive business impact.
Responsibilities
- Design, train, and evaluate machine learning models for production use cases.
- Implement end-to-end ML pipelines from data preprocessing to model serving and monitoring.
- Translate business requirements into ML solutions in collaboration with cross-functional teams.
- Debug and optimize model performance in production and iterate based on real-world feedback.
- Write clean, maintainable code and contribute to ML infrastructure and tooling.
- Participate in code reviews and share knowledge with the broader team.
Requirements
- 3+ years of professional experience in machine learning or software engineering with hands-on applied ML work in production systems.
- Strong fundamentals in model selection, feature engineering, evaluation, and validation.
- Experience building, deploying, and maintaining ML systems at scale including data pipelines, monitoring, A/B testing, and MLOps tools (e.g., cloud ML platforms, Kubernetes, Docker).
- Experience implementing end-to-end ML pipelines and deploying ML systems in production with experimentation frameworks.
- Ability to work in a fast-moving product environment with rapid iteration and ambiguity.
Skills
- Python for ML development
- TensorFlow
- PyTorch
- scikit-learn
- MLOps tools
Location
- San Francisco, California, United States
Work Type
- On-site
Experience Level
- 3+ years
Salary/Compensations
- Compensation details are provided by the employer upon request.