15 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 translate business needs 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 knowledge with teammates.
Requirements
- Completed university degree.
- At least three years of professional experience in machine learning or software engineering, including post-graduation employment.
- Hands-on experience building and deploying production ML systems in an ML engineering role at a startup or fast-moving product company.
- Strong understanding of model selection, evaluation metrics, feature engineering, and validation techniques.
- Proficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn.
- Experience maintaining production ML pipelines, including preprocessing, model serving, and monitoring at scale.
- Experience with MLOps tools or cloud platforms such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.
- Experience with production A/B testing or experimentation and optimizing models based on observed performance.
- Comfort working through ambiguity and iterating quickly in a product environment.
Benefits
- On-site work in San Francisco, California, United States.