Staff Software Engineer, Audiences
Posted
Summary
Staff engineer leading Fetch's audience business: building services and pipelines that turn purchase data into predictive and lookalike advertising audiences, productionizing ML models with Data Science, and delivering segments to advertisers and DSPs. US remote or office; needs 12+ years of data-intensive engineering experience.
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 building the capabilities to turn purchase insights into a scalable audience business. We want to help advertisers reach both known buyers and prospective customers through predictive and lookalike audiences, combining Fetch’s purchase data with licensed identity and attribute data to expand reach beyond our existing users.
We’re looking for a hands-on Staff Software Engineer to lead the engineering behind these products. You’ll partner with Data Science, Product, and Data Platform to take audience models from prototypes to reliable products that partners can activate through their advertising platforms.
This is a full-time role that can be held from one of our US offices or remotely in the United States.
Role Responsibilities:
- Build the audience lifecycle. Develop services and workflows for defining seed audiences, incorporating identity and attribute data, scoring eligible populations, validating membership, refreshing segments, and delivering audiences to partners and DSPs.
- Bring predictive and lookalike models into production. Partner with Data Science to build repeatable training, evaluation, deployment, and batch-scoring workflows. Make it straightforward to introduce new models, compare versions, and refresh audiences as data and behavior change.
- Make audience activation reliable. Integrate identity mappings and partner-specific delivery requirements. Track how audience coverage changes through matching and delivery, diagnose losses, and build repeatable integrations that support additional partners.
- Build quality and operational controls into the product. Version datasets, models, and audience definitions so results are reproducible. Implement monitoring, quality checks, safe retries, corrections, and access controls, including enforcement of data-use restrictions.
- Lead technical execution across the domain. Shape the audience engineering roadmap, make architecture and build-versus-reuse decisions, and guide other engineers through delivery. Build on shared ingestion, storage, orchestration, and governance capabilities with the core Data Platform team.
Minimum Requirements:
- 12+ years of professional software engineering experience, including ownership of data-intensive products from design through production adoption.
- Strong coding, API design, data modeling, and distributed-systems skills.
- Experience operationalizing ML or predictive systems, including data dependencies, evaluation workflows, deployment, batch scoring, and monitoring.
- Demonstrated experience setting technical direction for an engineering domain, aligning teams around architecture and priorities, and leading complex initiatives from ambiguous requirements through production adoption.
- Experience making and evolving architectural decisions for production systems, balancing near-term delivery with long-term reliability, scalability, operating cost, and maintainability.
- Ability to explain technical tradeoffs, build agreement across teams, and resolve dependencies and ownership boundaries.
- Experience increasing the effectiveness of other engineers through design reviews, mentoring, technical standards, and breaking complex initiatives into work that multiple engineers can deliver.
- A track record of delivering measurable outcomes and using production results to guide improvements.
- Experience with identity resolution, audience activation, adtech, or partner data integrations.
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 .
