1 day ago
San Francisco, CA, USAMid Level
Base Salary
$140k - $140k/yr
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.
- Partner with product and engineering teams to translate business needs into machine learning 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 knowledge with teammates.
Requirements
- At least 3 years of professional machine learning or software engineering experience, including building and deploying 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 end-to-end ML pipelines and maintaining deployed systems at scale.
- Familiarity with MLOps and cloud platforms or tools such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.
- Experience with production A/B testing or experimentation and debugging models based on operational feedback.
- Background in a startup or fast-moving, product-focused environment, with completed university studies and professional employment after graduation.
Benefits
- On-site work in San Francisco, California.
- Base salary starts at $140,000 USD annually; the upper end of the range was not provided.