6 hours ago
San Francisco, CA, USAMid Level
Responsibilities
- 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, engineering, and domain experts 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.
- Strong fundamentals in 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 for data preprocessing, model serving, and monitoring.
- Experience deploying and maintaining ML systems at scale with 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.
Benefits
- On-site work in San Francisco, California.