Life360 is looking to unlock step-function growth in users, subscribers, and revenue by building shared AI/ML capabilities that accelerate decision-making, experimentation, and automation across multiple product teams.
Requirements
- 5+ years of professional experience in building and deploying ML models in production
- Strong proficiency in Python or Java, model development libraries (e.g. PyTorch, TensorFlow, scikit-learn), and ML Ops tools
- Experience with serving ML models behind scalable APIs with low-latency performance requirements
- Ability to design, build, and manage real-time and batch data pipelines, ideally in collaboration with Data Engineering
- Knowledge of experiment design, A/B testing, and causal inference methods for ML product validation - bonus if you have experience with StatSig
- Familiarity with microservices architecture, containerization (Docker, Kubernetes), and modern deployment pipelines
- Bonus: Familiarity with streaming systems like Kafka
Responsibilities
- Partner with Product, Data Science, and Cloud Engineering to design and deploy ML models that power personalization, experimentation, and automation use cases
- Build, productionize, and maintain real-time model APIs for recommendations, predictive targeting, and generative AI experiences
- Work with backend and mobile engineers to integrate model outputs directly into user-facing features
- Contribute to the development of self-serve ML infrastructure to accelerate experimentation across product teams
- Design and implement feature pipelines, model training workflows, and scalable inference systems
- Collaborate on our long-term vision for a flexible, extensible ML platform that supports multiple use cases across Growth and Core product teams
- Monitor and maintain model health, performance, and drift in production
Other
- Bachelor’s degree in Computer Science, Machine Learning, Applied Math, or a similar quantitative field—or equivalent industry experience
- Comfortable collaborating cross-functionally with mobile, backend, and data platform teams
- Mentor other developers who are trying to grow
- Build technical specs with Staff engineers
- Handle on call rotation and address live incidents
