The ideal candidate combines strong product judgment, hands-on technical depth in software engineering, operational rigor, and customer-facing experience, with a passion for turning emerging model capabilities into scalable data products.
Own the roadmap and strategy for Scale's Coding portfolio, defining priorities across SFT and preference data, reinforcement learning environments, agentic task suites, and evaluation products.
Extend the SWE-Bench Pro and SWE Atlas franchises; deciding what comes next as agents saturate current tasks, and converting benchmark authority into training-data and environment revenue.
Facilitate exploration of the Coding domain and drive alignment among AI-PM, ML, Engineering, Operations, and GTM stakeholders.
Evaluate, prioritize, and operationalize new coding product proposals, ensuring alignment with customer demand, model capability frontiers, and company strategy.
Define and manage the end-to-end coding product lifecycle, from ideation and task taxonomy design to pilot, launch, scaling, and sunset decisions.
Partner with ML researchers and senior software engineers to develop trustworthy task specifications, rubric and grader design, verifiable reward signals, and quality bars for code correctness.
Drive the roadmap for coding infrastructure; Harbor-native container environments, execution sandboxes, reproducible repo images, automated verification, and contributor tooling — to improve scalability, reduce manual effort, and accelerate delivery.
Establish governance processes for data quality, contamination and leakage prevention, license and IP hygiene, reproducibility, auditability, and release management.
Grow and steward the expert contributor network of professional software engineers across languages, frameworks, and seniority levels, balancing throughput, cost, and quality.
Own and grow external partnerships across the coding ecosystem, including open-source benchmark collaborations, enterprise codebase partnerships, and joint releases.
Work directly with frontier AI labs and enterprise customers to understand where their coding models fail, gather feedback, and influence future investments.
