About the job
We're looking for a Senior AI Product Manager to own and scale this Coding portfolio from here. In this role, you will define the strategy, roadmap, and operational excellence of Scale's coding data products, RL environments, and agentic coding evaluations. You will work across AI Product Management, ML Researchers, Engineering, Operations, and Go-To-Market teams to turn our benchmark credibility into a durable, revenue-generating product line that leading labs depend on to train the next generation of software engineering agents.
Responsibilities
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.
Track adoption, usage, model-impact signals, and business outcomes, using data-driven insights to guide roadmap decisions and resource allocation.
Identify new coding capability areas; long-horizon agentic work, repo-scale refactoring, debugging, code review, migrations, low-resource languages; that strengthen Scale's leadership and create new revenue opportunities.
Collaborate closely with GTM teams to support customer engagements, thought leadership initiatives, product launches, and strategic partnerships.
Qualifications
Minimum
5+ years of experience in product management, technical program management, consulting, or customer-facing technical roles.
Strong technical depth in software engineering; you can read code, reason about a codebase, and hold your own with senior engineers and ML researchers.
Familiarity with how coding models are trained and evaluated, including post-training methods, agentic scaffolds and harnesses, and coding benchmarks such as SWE-Bench and Terminal-Bench.
Experience building and scaling products that require coordination across engineering, operations, and business teams.
Excellent stakeholder management and executive communication skills, with a demonstrated ability to drive alignment across cross-functional organizations.
Strong analytical skills and the ability to translate ambiguous market and customer signals into clear product strategy.
Preferred
Experience working with AI researchers, ML teams, developer tools, or human-in-the-loop data pipelines is strongly preferred.
Entrepreneurial mindset with a track record of creating new products, programs, or business initiatives from the ground up.
Bias for action and comfort operating in fast-moving, ambiguous environments.
Passion for advancing AI coding capability and helping define how the industry builds and measures software engineering agents.