4 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 for data preprocessing, model serving, and monitoring.
- Translate business requirements into ML solutions with product, engineering, and domain partners.
- Debug and improve production model performance using monitoring and real-world feedback.
- Write maintainable code and contribute to ML infrastructure and tooling.
- Participate in code reviews and share knowledge with teammates.
- Run or support production A/B testing and experimentation and optimize models based on results.
Requirements
- At least 3 years of professional machine learning or software engineering experience, including building and deploying production ML systems.
- Professional machine learning engineering experience beyond solely data science work, with substantive employment after completing studies.
- A completed degree and understanding of model selection, evaluation metrics, feature engineering, and validation.
- Proficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn.
- Experience implementing and maintaining production ML pipelines covering preprocessing, serving, and monitoring.
- Familiarity with MLOps tools and cloud platforms such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.
- Experience with production A/B testing or experimentation frameworks.
- Experience in a startup or fast-moving product environment and ability to prioritize impact through ambiguity.
Benefits
- On-site role based in San Francisco, California, United States.