Principal Machine Learning Engineer

Fetch•Reston, VA

1d•Hybrid

About The Position

Fetch is entering its AI-first era, and we're looking for a Principal Machine Learning Engineer to design, scale, and evolve the intelligent systems that power personalization, relevance, and ranking across our platform. You will build the ML infrastructure and real-time learning systems that enable Fetch to serve more relevant, adaptive, and high-performing experiences for millions of users. Operating at the intersection of ML infrastructure, personalization, and large-scale distributed systems, you will be a key technical force shaping how Fetch's ML systems evolve. You'll collaborate with Product, Data Science, Platform, and Engineering teams to drive clarity, define architectural standards, and ensure our ML systems become smarter, faster, and more adaptive to evolving user preferences over time. This is a hands-on, high-impact technical leadership role with influence across multiple engineering collectives. Your work will shape how Fetch builds and scales ML infrastructure: personalization, search, ranking, real-time learning, and feature systems at consumer scale.

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 for ML systems.
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

Design and evolve the ML infrastructure supporting personalization, search, ranking, and ad tech. Build systems that prioritize relevance, adaptability, and measurable user value.
Design and implement zero-to-one systems, including real-time learning and data pipelines. Define architectural patterns for feature infrastructure, model serving, and low-latency, high-throughput decision-making at consumer scale.
Advance the core systems powering personalization and ranking, including data infrastructure, distributed systems, and large-scale data pipelines. Pioneer new approaches that raise both model performance and system efficiency.
Drive technical design, architecture, and cross-team alignment for major ML initiatives. Partner with product and engineering teams to create dynamic systems that adapt to evolving user preferences, and translate architectural tradeoffs into measurable outcomes.
Improve streaming and real-time learning infrastructure to enable faster iteration across ranking, personalization, and search systems.
Use AI tools to accelerate your work, including designing features and validating ideas with ChatGPT and Claude sandboxes, leveraging AI for code generation and technical prototyping, using AI assistants for systems architecture diagramming and design validation, and exploring LLMs to enhance personalization, conversational search, and feature creation.
Coach senior engineers and rising technical leads, elevating standards for architectural clarity, technical execution, and design quality. Help raise the bar across the team and amplify impact through reusable frameworks and technical patterns.
Operate effectively in high levels of ambiguity with minimal direction, prioritizing effectively and driving impact. Shape the engineering culture around thoughtful, zero-to-one system design.