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Salary: USD 233,500 - 290,700 / annual
Babylist is seeking a Staff Machine Learning Engineer to own personalization across the platform. You will set direction for how personalization works across the homepage feed, recommendations, search, and foundational representations that multiple teams build upon. This is a high-impact individual contributor role where you maintain hands-on involvement in the code while guiding technical strategy.
Key responsibilities include:
- Taking fuzzy business problems from conception through production, owning whether they deliver customer value
- Building custom embeddings from raw data—domain-specific representations beyond off-the-shelf models—and maintaining them as multiple surfaces adopt them
- Making modeling and architecture decisions that span teams and have long-term implications
- Owning the full ML lifecycle: orchestration, deployment, monitoring, and retraining loops
- Setting standards for how personalization integrates with AI, including building evaluation frameworks to catch model failures before production
- Partnering with product, design, and data teams as a peer from problem definition through shipping
- Coaching senior engineers through complex technical and ambiguous decisions
Current focus areas include building foundational embeddings shared across surfaces, resolving customer identity across registry/shop/health to enable cross-platform recommendations, and determining registry recommendations with live experiment validation.
Babylist is a consumer platform serving millions of families with registry, shopping, financial, maternal health, and education tools. The company generated $750M+ in revenue in 2025 (up 45% YoY) and has been profitable for eight years while remaining independent. The engineering team is approximately 65 people, keeping scope visible and wide.
The company operates remote-first across the US and Canada with in-person gatherings twice yearly. Teams are small pods of 3-5 engineers. The tech stack includes Rails, React, TypeScript, Sidekiq, MySQL, Snowflake, dbt, Airflow, Weaviate, AWS SageMaker, MLflow, and AI tooling like Claude Code and Devin. The architecture is intentionally simple—one Rails monolith with few moving parts—to enable speed and AI reasoning.
QUALIFICATIONS & REQUIREMENTS:
- Multiple years of shipped production ML experience with strong, loosely-held opinions
- Demonstrated ability to pick up ambiguous problems and move forward independently
- Track record of changing how a team builds with AI in a way that stuck
- Built recommender systems or personalization reaching real users at scale, with measurable impact
- Deep expertise in the Python ML ecosystem (pandas, scikit-learn, XGBoost, PyTorch)
- Fluency across the full ML lifecycle: orchestration, training, monitoring—not just model training
- Ability to build custom representations from raw data rather than relying solely on off-the-shelf embeddings
- Preference for measuring yourself by customer outcomes or foundational models other teams depend on
- Comfort with zero-to-one problem definition, architecture, and end-to-end ownership
- Curiosity to spot problems early and push ideas to shipping
