Strategic Project Lead - Code

About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com. The Role You will own the production system behind Turing’s software-engineering data programs, turning complex research requirements into predictable delivery across quality, throughput, contributor performance, timelines, and cost. These programs may involve supervised coding demonstrations, repository-level tasks, agentic trajectories, reinforcement-learning environments, benchmarks, code review, and rubric-based evaluations. They can require coordinating hundreds of distributed software engineers while responding quickly to changing research requirements. This is an operations leadership role with a meaningful technical bar. You must be able to inspect code, understand tests, interrogate quality signals, and challenge a workflow or rubric when it is not producing the intended result. You will not be expected to act as the principal engineer for every program. Your primary responsibility is to build and operate the system that consistently produces high-quality technical work at scale. What You’ll Own 1) Operational execution — own end-to-end delivery on every project you run • Design and manage data pipelines from customer specification to final delivery, with full accountability for scope, timeline, and quality. • Scope and stand up coding workstreams across supervised demonstrations, agentic trajectories, RL environments, benchmark construction, and rubric-graded evaluation. • Diagnose bottlenecks in real time — re-sequence workflows, refine instructions, create incentive systems, and scale review processes to hit throughput targets. 2) Quality ownership — ensure world-class data integrity on every project • Own quality control across the annotation lifecycle: set the bar, measure against it, and close the gap when it slips. • Analyze datasets to identify trends, anomalies, and systematic errors — then fix the root cause, not just the symptom. • Implement and continuously improve annotation, evaluation, and curation best practices. 3) Large-scale coordination — orchestrate the work of 100–1,000+ contributors • Define the required contributor profile and partner with talent teams to source, assess, onboard, and ramp distributed software engineers. • Own contributor training, performance management, reviewer capacity, incentives, and corrective actions. • Build team-lead and reviewer structures that maintain execution standards across programs involving hundreds of contributors. 4) Customer relationships — be the face of Turing to the world’s leading AI labs • Act as the primary point of contact for researchers and program managers at frontier AI labs providing clear reporting on progress, quality, risks and recovery actions. • Translate research intent into a task specification, and push back when a spec will not produce the signal the researcher actually wants. • Build the kind of long-term trust that converts a one-off project into a multi-year partnership — and identify expansion opportunities along the way. 5) Playbook building — codify what works so future SPLs scale faster than you did • Use Python, SQL or other appropriate tools to automate quality sampling, defect classification, throughput analysis, and weekly reporting. • Turn successful workflows into reusable playbooks, quality controls, evaluation assets, and contributor-management systems. • Share lessons and mentor other SPLs so each program improves the operating system for the next one.…