2mo ago

Principal Scientist [Machine Learning Engineer] - Ads Platform

OtherMachine Learning EngineerDataScientist

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Quick Summary

Key Responsibilities

Architect systems that translate advertiser goals—such as incremental sales, customer acquisition, or return on ad spend—into optimization objectives, delivery strategies, and measurable outcomes.

Requirements Summary

bidding, budget allocation, pacing, conversion prediction, ad ranking, audience optimization, or campaign optimization. Strong understanding of how a

Technical Tools

OtherMachine Learning EngineerDataScientist

About the Role:

Fetch is building an AI-powered, objective-based ad buying platform that helps advertisers turn business goals into measurable outcomes. We’re looking for a Principal Machine Learning Engineer to architect and build the intelligence behind that platform—from translating advertiser objectives into optimization strategies to making real-time decisions about ad delivery, bidding, and budget allocation.

You will help create a buying experience where advertisers define what they want to achieve, and our systems determine how to achieve it. Your work will connect prediction, optimization, experimentation, and measurement in a continuous learning loop that improves advertiser performance while delivering relevant experiences for Fetch users.

We’re looking for someone with hands-on experience building automated advertising products such as Meta Advantage+, Google Performance Max, or comparable objective-based buying platforms. You understand how these systems work beneath the advertiser interface and have built the models, decision systems, or infrastructure that power their performance.

This is a hands-on technical leadership role with influence across Product, Data Science, Platform, and Engineering. You’ll set architectural direction, write production code, and lead zero-to-one development of systems central to Fetch’s advertising business.

Role Responsibilities

Build Objective-Based Ad Buying: Architect systems that translate advertiser goals—such as incremental sales, customer acquisition, or return on ad spend—into optimization objectives, delivery strategies, and measurable outcomes.

Develop Automated Optimization Systems: Design and implement the prediction and decision systems powering bidding, budget allocation, pacing, audience selection, and ad ranking. Build feedback loops that continuously adapt delivery based on observed performance.

Connect Measurement to Optimization: Partner with Data Science and Product to incorporate conversion signals, attribution, incrementality, and experimentation into optimization systems. Address challenges such as delayed outcomes, sparse signals, and uncertainty in performance measurement.

Architect for Real-Time Scale: Build the data pipelines, feature infrastructure, model serving, and low-latency decision systems required to support advertising at consumer scale. Balance model quality, reliability, latency, and infrastructure cost.

Advance Learning and Experimentation: Develop systems for rapid experimentation, model iteration, and performance evaluation. Apply approaches such as online learning, exploration and exploitation, and constrained optimization where they improve advertiser outcomes.

Lead Through Product and Technical Partnership: Work with Product to define platform capabilities, performance goals, and advertiser controls. Translate complex optimization tradeoffs into clear product decisions and scalable technical designs.

Own Zero-to-One Execution: Take ambiguous problems from initial design through production launch and ongoing improvement. Stay close to implementation, diagnose performance issues across the stack, and establish reusable architectural patterns.

Mentor and Multiply Engineering Impact: Coach senior engineers and technical leads, raise standards for system design and execution, and build shared expertise in advertising ML and optimization.

Accelerate Engineering with AI: Use AI-assisted development, prototyping, and analysis to shorten iteration cycles and improve engineering productivity.

Minimum Requirements

Hands-on experience building and shipping automated, objective-based advertising products such as Meta Advantage+, Google Performance Max, or comparable platforms. Experience should include developing underlying ML, optimization, or delivery systems.
Proven experience building production systems in one or more core advertising domains: bidding, budget allocation, pacing, conversion prediction, ad ranking, audience optimization, or campaign optimization.
Strong understanding of how advertiser objectives, optimization signals, delivery constraints, and measurement interact to drive campaign performance.
Deep expertise in distributed systems, ML infrastructure, large-scale data pipelines, and production model serving.
A track record of delivering measurable improvements in advertiser outcomes through ML and optimization.
Strong software engineering and system design skills, with experience setting architectural direction while remaining hands-on in implementation.
Experience leading zero-to-one initiatives and aligning Product, Data Science, and Engineering around complex technical decisions.
Experience mentoring senior engineers and elevating technical quality across teams.
Ability to operate independently in ambiguous environments, prioritize effectively, and carry initiatives through launch and iteration.

Preferred Requirements

Direct engineering experience on Meta Advantage+, Google Performance Max, or a similarly automated ad buying product.
Experience optimizing toward purchase outcomes, customer acquisition, return on ad spend, or incremental business results.
Expertise in constrained optimization, auction systems, contextual bandits, reinforcement learning, or other methods used in automated ad delivery.
Experience with attribution, incrementality testing, causal inference, and conversion modeling.
Experience addressing delayed conversions, sparse feedback, cold starts, and exploration in advertising systems.
Experience building streaming features, online learning systems, and real-time feedback loops.
Familiarity with retail media, commerce advertising, or optimization using transaction and purchase signals.

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.