About the Role
Build and deploy production machine learning systems in a fast-moving product environment. You will own work across the ML lifecycle, partnering with product, engineering, and domain experts to deliver models that address real business needs.
What You'll Do
Design, train, and evaluate machine learning models for production use cases.
Build end-to-end ML pipelines, from data preprocessing through model serving and monitoring.
Work with product and engineering partners to turn business requirements 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 technical knowledge with teammates.
What We're Looking For
At least 3 years of professional machine learning or software engineering experience, including hands-on ownership of production ML systems in a startup or fast-moving product environment.
Strong ML fundamentals, including model selection, evaluation, feature engineering, and validation.
Proficiency in Python and hands-on experience with TensorFlow, PyTorch, or scikit-learn.
Experience implementing production ML pipelines that cover data preprocessing, model serving, and monitoring.
Experience deploying and maintaining ML systems at scale with tools or platforms such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.
Experience with production A/B testing or experimentation frameworks, and optimizing models based on results.
A completed university degree and professional employment experience after graduation.
Ability to work through ambiguity and prioritize practical impact in a dynamic environment.
Location
On-site in San Francisco, California.