USAStaff Backend Engineer, Customer Value Optimization
2 нед. назад
USALead
pythonml modelsapIssystem design
Staff Backend Engineer role focused on building scalable infrastructure to serve ML models in production, bridging Data Science and Backend Engineering teams.
Обязанности
- We're looking for a Staff Backend Engineer to join our Customer Value Optimization (CVO) tribe at . This is a high-impact individual contributor role sitting at the intersection of Data Science and Backend Engineering — you'll be the technical bridge that turns ML models into reliable, scalable production systems.
- You'll work across squads within the CVO tribe, partnering closely with Data Scientists and Backend Engineers to define how we serve ML models at scale, raise the technical bar, and accelerate the tribe's ability to deliver value to our customers.
Будет плюсом
- Experience with ML frameworks and serving tools
- Familiarity with feature stores, model registries, or MLOps tooling
- Background in e-commerce, subscription, or personalization domains
Другое
- Design, build, and own robust, scalable infrastructure for live serving of ML models in production
- Act as the technical bridge between Data Science and Backend Engineering teams within the CVO tribe — translating Data Science needs into scalable engineering solutions
- Define and drive best practices for model serving, APIs, and system design across squads
- Lead cross-squad technical initiatives, from architecture decisions to hands-on implementation
- Partner with Data Scientists to ensure models are production-ready — performant, observable, and maintainable
- Contribute to tribe-wide engineering standards and shape the technical roadmap
- Mentor and support engineers across squads without direct line management responsibility
- 7+ years of backend engineering experience, with a strong foundation in Python
- Proven experience building and operating ML model serving systems or ML platforms in production
- Deep understanding of distributed systems, APIs, and micro services architecture
- Strong track record of working across team boundaries to deliver complex, cross-functional projects
- Excellent communication skills — able to work fluently with Data Scientists, Machine Learning Engineers, and Data Engineers
- A collaborative, low-ego approach with strong technical judgment