Build and deploy machine learning systems for a fast-moving technology product team. You will own work across the ML lifecycle, from defining problems to monitoring models in production, partnering with product, engineering, and domain experts to deliver useful outcomes.
Design, train, and evaluate machine learning models for production use cases.
Build end-to-end ML pipelines spanning data preprocessing, model serving, and monitoring.
Work with product and engineering partners to turn 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.
A completed university degree and at least 3 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 implementing and 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.
On-site in San Francisco, California, United States.