Meet Fetch Engineering
At Fetch, engineering is driven by curiosity, ownership, and a bias toward action. We operate in complex problem spaces where the right answer is not always clear, and success depends on adaptability, critical thinking, and informed decision-making. Our engineers are comfortable navigating ambiguity, understanding tradeoffs, gathering context, and turning uncertainty into progress while maintaining high technical standards.
Engineers at Fetch take pride in building reliable, scalable systems that serve millions of users. You will contribute directly to the codebase, collaborate closely with cross-functional partners, and help shape best practices that elevate the quality of our work. We foster a culture of mentorship and collaboration, where engineers grow by learning from one another and holding a high bar for quality, reliability, and impact.
About the Role:
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.
Role Responsibilities
Architect for Scale and Intelligence: Design and evolve the ML infrastructure supporting personalization, search, ranking, and ad tech. Build systems that prioritize relevance, adaptability, and measurable user value.
Advance Intelligence in Production: 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.
Evolve the ML Platform: 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.
Lead Through Influence and Product Partnership: 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.
Scale Experimentation & Learning Systems: Improve streaming and real-time learning infrastructure to enable faster iteration across ranking, personalization, and search systems.
Accelerate Innovation Velocity: 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.
Mentor & Multiply Engineering Impact: 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.
Model Technical Excellence: 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.
Minimum 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.
Preferred Requirements
- 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.
This role can be based in one of our US offices or remotely within the United States.
Compensation: At Fetch, we offer competitive compensation packages including base, equity, and benefits to the exceptional folks we hire. Discover our benefits and how our employees live rewarded at .