Principal Machine Learning Engineer

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 https://fetch.com/careers

[https://fetch.com/careers].