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About the Role
Build and deploy production machine learning systems in a fast-moving product environment. You will own work across the ML lifecycle, partnering with product, engineering, and domain experts to deliver models that address real business needs.
Responsibilities
- Design, train, and evaluate machine learning models for production use cases.
- Build end-to-end ML pipelines, from data preprocessing through model serving and monitoring.
- Work with product and engineering partners to turn business requirements into ML solutions.
- Debug and improve production model performance using monitoring data and real-world feedback.
- Write maintainable code and contribute to ML infrastructure and tooling.
- Participate in code reviews and share technical knowledge with teammates.
Requirements
- At least 3 years of professional machine learning or software engineering experience, including hands-on ownership of production ML systems in a startup or fast-moving product environment.
- Strong ML fundamentals, including model selection, evaluation, feature engineering, and validation.
- Proficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn.
- Experience implementing production ML pipelines that cover data preprocessing, model serving, and monitoring.
- Experience deploying and maintaining ML systems at scale with tools or platforms such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.
- Experience with production A/B testing or experimentation frameworks, and optimizing models based on results.
- A completed university degree and professional employment experience after graduation.
- Ability to work through ambiguity and prioritize practical impact in a dynamic environment.
Skills
- Python
- TensorFlow
- PyTorch
- scikit-learn
- AWS SageMaker
- GCP Vertex AI
- Kubernetes
- Docker
Location
- San Francisco, California
Work Type
- On-site
Experience Level
- 3+ years
Education Level
- University degree