3 hours ago
San Francisco, CA, USAMid Level
Responsibilities
- Design, train, evaluate, and deploy machine learning models for production use cases.
- Build end-to-end ML pipelines covering data preprocessing, model serving, and monitoring.
- Partner with product and engineering teams to translate business needs into ML solutions.
- Debug and improve model performance using production monitoring and real-world feedback.
- Write maintainable code and contribute to ML infrastructure, tooling, and code reviews.
- Share knowledge with teammates and support rapid iteration.
Requirements
- At least 3 years of professional machine learning or software engineering experience, including building and deploying production ML systems.
- A completed degree and strong machine learning fundamentals in model selection, evaluation metrics, feature engineering, and validation.
- Proficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn.
- Experience implementing production ML pipelines with data preprocessing, model serving, and monitoring.
- Familiarity with MLOps tools and cloud platforms such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.
- Experience maintaining and optimizing deployed systems and using A/B testing or other production experimentation methods.
- Comfort working in a fast-moving product environment with ambiguity and changing priorities.
Benefits
- This is an on-site role based in San Francisco, United States.