About The Position

Fetch is building the future of personalized consumer experiences. We’re looking for a Principal Machine Learning Engineer to design and scale systems that power personalization, relevance, and ranking across our platform. This is a high-impact role where you’ll drive new initiatives, mentor other engineers, and shape the technical direction of ML at Fetch. This is a full-time role that can be held from one of our US offices or remotely in the United States.

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.

Nice To Haves

  • Familiarity with LLMs and their application in personalization, feature creation, and conversational search.
  • Experience with streaming/real-time learning systems.
  • Exposure to conversational search or large-scale information retrieval.
  • Previous work bridging model development with real-time serving systems.

Responsibilities

  • 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.
  • 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.
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