Machine Learning Engineer

Responsibilities

• Design, train, and evaluate machine learning models for production use cases.
• Build end-to-end ML pipelines for data preprocessing, model serving, and monitoring.
• Translate business requirements into machine learning solutions with product and engineering teams.
• Debug and optimize model performance in production using real-world feedback.
• Write maintainable code and contribute to ML infrastructure and tooling.
• Participate in code reviews and share knowledge across the team.

Requirements

• 3–7 years of professional experience in machine learning or software engineering with hands-on applied ML work in production systems.
• Experience working in a startup or similarly fast-paced, resource-constrained environment.
• Strong fundamentals in model selection, evaluation, feature engineering, and validation.
• Proficiency in Python and common ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Experience building, deploying, and maintaining ML systems at scale rather than only academic or prototype systems.
• Experience with data pipelines, feature engineering, or model evaluation in production contexts.
• Familiarity with cloud ML platforms or MLOps tooling such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker is preferred.
• Experience with A/B testing, experimentation frameworks, or production model monitoring is preferred.
• Ability to prioritize impact and work comfortably with ambiguity in a dynamic environment.

Benefits

• Based in San Francisco, California; specific work arrangement should be confirmed directly with the team.