About this role
This role is designed for an experienced Machine Learning Engineer who can take ownership of the core personalization systems that drive user engagement across Babylist's platform. The challenge lies in moving beyond standard implementations to tackle genuinely difficult problems, such as developing foundational embeddings and ranking systems that serve multiple product surfaces. The individual will be responsible for defining the direction of personalization, deciding which modeling bets to pursue, and ensuring that the deployed models deliver measurable customer impact. This position exists to bridge the gap between abstract business needs and robust, production-ready AI solutions, especially in areas like financial support, maternal health, and educational resources. It is ideal for someone who thrives on owning ambiguous problems end-to-end and setting the technical standard for how personalization is built using AI.
The Staff MLE will operate at a level where they influence strategy rather than just executing tasks. They will be directly involved in defining the architecture for personalization across the homepage feed, recommendations, and search, ensuring these systems are built on shared, high-quality representations. This is not a role focused on routine model training; instead, it requires deep expertise in the ML lifecycle, from initial fuzzy problem definition through to deployment, monitoring, and establishing rigorous evaluation standards. The successful candidate will be someone who can move from a high-level concept to a production model and remain accountable for its real-world performance.
A strong fit for this role is a self-starter who measures success by customer outcomes and is comfortable operating in a highly ambiguous environment. They must possess the ability to define the problem space, architect novel solutions from scratch, and coach other engineers through complex technical and strategic decisions. The ideal candidate is deeply familiar with the Python ML ecosystem and has a proven track record of deploying personalization systems at scale that have demonstrably moved key user metrics.
What you will do
- Set the strategic direction for the personalization domain, owning models for the homepage feed, add-next recommendations, and search personalization.
- Define the foundational representations that multiple teams will build upon across the platform.
- Take fuzzy business problems from initial concept through to a production-ready model, staying engaged to validate customer impact.
- Build custom embeddings from raw data to create domain-specific representations that are adopted across multiple surfaces.
- Make the necessary modeling and architectural decisions that span across different teams and are difficult to reverse.
- Own the complete lifecycle of models, including orchestration, deployment, monitoring, and the retraining loop.
- Establish the standards for how personalization is built using AI and define evaluation metrics to catch incorrect models before deployment.
- Partner with product, design, and data teams to shape the roadmap and determine what initiatives are worth building.
What you need
- Demonstrated experience shipping production Machine Learning models to reach users at scale is required.
- Must possess strong opinions in the field and the ability to apply them flexibly to ambiguous problems.
- Proven ability to take an ambiguous problem and initiate movement before receiving complete specifications.
- Deep proficiency in the Python ML ecosystem, including libraries such as pandas, scikit-learn, XGBoost, and PyTorch.
- Fluency across the entire ML lifecycle, encompassing orchestration, deployment, and monitoring, not just training.
- Experience building custom representations from raw data, moving beyond reliance on off-the-shelf embedding models.
- A history of building recommender systems or personalization features that resulted in measurable user movement.
- Ability to coach senior engineers through complex technical and strategic challenges.
Nice to have
- Experience with agentic systems
Benefits
- $233,500 to $290,700 salary range
- Target annual bonus of 20 percent of base
- Meaningful equity
- 401(k) match
Skills and tech stack
Questions candidates ask
What is the remote policy?
The posting does not explicitly detail the remote work policy for this role.
What is the compensation structure?
The starting base salary range for a US-based Staff Engineer is between $233,500 and $290,700. This is supplemented by a target annual bonus of 20 percent of the base salary.
What is the core technology stack?
The required technical skills include deep proficiency in the Python ML ecosystem, specifically mentioning pandas, scikit-learn, XGBoost, and PyTorch.
What is the team structure like?
The role involves partnering with product, design, and data as a peer, and coaching senior engineers through difficult calls.