Machine Learning Engineer

What you'll do

• Design, prototype, and ship models for predictive, ranking, generative, and agentic systems.
• Implement RAG, embeddings, fine-tuning, and in-product copilots for creative use cases.
• Define "good" metrics and build offline and online evaluation harnesses to detect quality regressions.
• Build MLOps foundations including feature pipelines, experiment tracking, versioning, and CI/CD.
• Develop automated retraining and monitoring systems to ensure model reliability in production.
• Translate research into shipped features by scoping feasibility with Product and Engineering teams.
• Manage the full lifecycle of models from initial problem framing to production deployment.

What we're looking for

• Bachelor's degree in a quantitative field or equivalent practical experience.
• 5+ years of experience building, deploying, and operating ML systems in production at scale.
• Strong foundation in feature engineering and shipping supervised/unsupervised models for prediction, ranking, recommendation, or personalization.
• Experience with modern GenAI including LLMs, RAG, embeddings, fine-tuning, and agent design.
• Fluency in Python and SQL along with proficiency in the broader ML tooling and infrastructure ecosystem.
• Ability to design and operate rigorous offline and online evaluation and experimentation frameworks.
• Ability to communicate technical work clearly to non-technical Product and Design partners.
• Preferred: Master's or PhD in a quantitative discipline and experience with Databricks or similar production environments.