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.

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.