Staff Software Engineer, Ads Measurement Signal

The Conversion Visibility pod enables a performant ads marketplace and helps prove value to advertisers by connecting on-Pinterest intent with offsite conversions in a privacy-preserving way. We are hiring a Staff Software Engineer to lead the backend architecture and implementation of a GenAI-powered Conversion Health agent, using Event Quality Scores and conversion data to proactively detect issues, recommend and automate fixes, and demonstrate measurable performance impact for advertisers. 

What you’ll do: 

• You’ll operate in a forward-deployed style, partnering directly with internal downstream teams that consume our conversion signal data to understand their workflows, build tailored solutions, and own them end-to-end from design through integration and iteration.
• Own the design and implementation of the Conversion GenAI agent: services, data flows, and retrieval layers that let agents reason over EQS, conversion funnels, and their impact on performance at scale.
• Evolve the existing GenAI Applications: harden prompts and tools, improve retrieval quality, add evaluation and safety checks, and make the agent reliable enough for always-on monitoring and decision support and make the impact on ad performance and efficiency clear.
• Design how the sub-agent connects with downstream ads products (PCL, ROAS bidding) and internal tools, including APIs, contracts, and workflows that surface product‑aware alerts and ranked recommendations to identify opportunities and power performance lifts.
• Consolidate and structure the measurement context layer—matched and attributed conversion tables, enrichment pipelines, and existing tools—into high-quality, AI-consumable signals the agent can query and reason over.
• Partner with Product, Operations, Sales, and other ads product teams to translate measurement pain points into agent skills (e.g., diagnosing EQS drops, PCL readiness, partner‑specific issues) and iterate quickly on internal-first experiences before expanding to advertiser-facing use cases.
• Lead an evolving GenAI culture and collaboration model across orgs: navigate ambiguity in a rapidly changing AI landscape to identify high-ROI patterns, codify pragmatic guardrails and workflows, and evolve how we collaborate so AI tooling translates into sustained gains in engineering velocity, measurement quality, and advertiser performance—not just isolated experiments.

What we’re looking for:

• 8+ years of backend or full-stack software engineering experience building large-scale distributed systems, services, and data pipelines, ideally in ads, measurement, or similar data-intensive domains.
• Proven track record shipping GenAI- or ML-powered products end-to-end (agent or model integration, retrieval, evaluation, safety/guardrails, and online/offline metrics).
• Required prior ads domain expertise, preferably in measurement, including conversion tracking, attribution, signal enrichment pipelines, and familiarity with concepts like ROAS, CPA, and campaign optimization. Strong bias toward business outcomes, with a track record of tying technical work to performance, measurement quality, and operational efficiency metrics.
• Strong proficiency in product scoping, data analysis and experimentation (e.g., SQL over large datasets, experiment design, cohort analysis) to connect conversion-health interventions to performance outcomes like ROAS, CPA, and PCL validity.
• Demonstrated technical leadership across teams: scoping ambiguous problems, aligning with product and XFN partners, and driving complex initiatives from vision through launch with clear documentation and communication.
• Experience upleveling engineers in AI tooling and best practices, including setting team-wide standards, templates, or processes for building and evaluating AI-assisted workflows.
• Bachelor’s degree in Computer Science, Engineering, a related field, or equivalent experience.  

Relocation Statement: 

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

In-Office Requirement Statement: 

• We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
• This role will need to be in the office for in-person collaboration1 time per week and therefore needs to be in a commutable distance from one of the Seattle offices.

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