Engineering Manager, Payments Risk Platform
Coinbase
Distributed Systems
Principal Software Engineer, Machine Learning
Fetch Rewards
full-time
Posted on:
Origin: • 🇺🇸 United States
Visit company websiteJob Level
Lead
Tech Stack
Distributed Systems
About the role
- Build and scale ML infrastructure for personalization, search, ranking, and ad tech at consumer scale.
- Design and implement zero-to-one systems, including real-time learning and data pipelines.
- Lead technical design, architecture, and cross-team alignment for major ML initiatives.
- Mentor engineers and help raise the bar on technical execution and design quality.
- Partner with product and engineering teams to create dynamic systems that adapt to evolving user preferences.
- Use AI tools to accelerate work: designing features and validating ideas with ChatGPT & Claude sandboxes; leveraging AI for code generation and technical prototyping; using AI assistants for systems architecture diagramming and design validation; exploring LLMs to enhance personalization, conversational search, and feature creation.
- Drive new initiatives, shape the technical direction of ML at Fetch, and deliver high-impact ML systems from scratch.
Requirements
- Proven experience building and scaling ML infrastructure in support of personalization, relevance, search, or ad tech systems.
- Deep hands-on expertise in data infrastructure, distributed systems, and large-scale data pipelines.
- Experience working at a consumer product company with ML models operating at scale.
- Prior contributions to ranking, personalization, or ad tech systems with measurable business impact.
- Strong systems design skills, with a track record of leading architecture and communicating design tradeoffs.
- Experience mentoring and elevating other engineers.
- Success leading zero-to-one technical initiatives and delivering new infrastructure or ML systems from scratch.
- Ability to operate in high levels of ambiguity with minimal direction, prioritizing effectively and driving impact.
- Preferred: Familiarity with LLMs and their application in personalization, feature creation, and conversational search.
- Preferred: Experience with streaming/real-time learning systems.
- Preferred: Exposure to conversational search or large-scale information retrieval.
- Preferred: Previous work bridging model development with real-time serving systems.
