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About the Role
Build and deploy ML systems for a fast-moving product, contributing across the full ML lifecycle and collaborating with product and engineering teams to ship impactful models.
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.
- Collaborate with product and engineering teams to translate business requirements into ML solutions.
- 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 in production systems.
- Experience in startup or fast-moving product environments with rapid iteration cycles.
- Strong fundamentals in model selection, evaluation, feature engineering, and validation.
- Proficiency in Python or similar languages and ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Experience building, deploying, and maintaining ML systems at scale including A/B testing, monitoring, data pipelines, and MLOps tools such as cloud ML platforms and container technologies.
- Comfort with ambiguity and ability to prioritize impact in a dynamic setting.
Skills
- Python
- TensorFlow
- PyTorch
- scikit-learn
- A/B testing
- Monitoring
- Data pipelines
- MLOps
- Cloud ML platforms
- Container technologies
Location
- San Francisco, CA
- San Francisco, California, United States
Work Type
- On-site
Experience Level
- 3+ years