Babylist is looking to solve the problem of improving user experience and business outcomes through machine learning and personalization efforts, specifically by developing recommender systems and AI-powered solutions to drive product and content discovery for millions of users.
Requirements
- Hands-on experience taking machine learning models from prototype to scalable, production-ready deployments
- Deep expertise in SQL and Python for machine learning applications
- Experience with distributed training frameworks, workflow orchestration (e.g., Airflow), and model monitoring tools
- Proven track record of delivering production-grade ML solutions, ideally in recommender systems, personalization, or similar consumer-facing products
- Understanding of the ML lifecycle end-to-end: experimentation, validation, rollout, and monitoring
- Relevant domain experience from consumer products such as e-commerce, dating apps, or platforms with complex user journeys
- Experience with React, Ruby on Rails, AWS, Sidekiq, MySQL, Redis, Native iOS and Android
Responsibilities
- Establish Babylist’s first machine learning function—defining the roadmap, practices, and long-term vision for personalization at scale
- Translate complex data into high-leverage personalization features that directly improve product discovery, user engagement, and business outcomes
- Accelerate experimentation and deployment by leveraging Babylist’s mature data infrastructure and close partnerships with analytics, engineering, and product leaders
- Deliver high-impact ML systems end-to-end, from design to production, with clear visibility into results for millions of users
- Shape Babylist’s ML culture by setting technical standards, mentoring peers, and influencing the organization’s broader AI strategy
- Lead technical development within a focused product pod (Product Manager, Software Engineer, occasional Designer), architecting the models behind our homepage feed, “add next” experience, and more
- Take ownership of the full ML feature lifecycle—from framing problems with stakeholders to deployment, monitoring, and iteration
Other
- Highly autonomous, able to define problem spaces, architect solutions from zero to one, and operate with strong ownership
- Ability to work in a small, agile pod while also helping shape the broader ML strategy across the organization
- Strong communication and collaboration skills, with ability to work with stakeholders to frame problems and deploy solutions
- Ability to work at a sustainable pace, with a focus on work/life balance
- Bachelor's degree or higher in a relevant field (not explicitly mentioned but implied)
