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.
Job description provided by Clera.
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