Strategic Project Lead, Software Engineering

Turing

1d•$120,000 - $500,000•Remote

About The Position

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.

Requirements

Background in consulting, finance, startups, or other operationally intense environments, with a proven track record of managing complex, multi-stakeholder projects.
Strong analytical and communication abilities: you can spot a bottleneck in a noisy production environment, build a measurement plan, and communicate the fix to a demanding client in plain language.
Customer-facing experience: comfortable working directly with high-profile clients, managing expectations, and building long-term relationships.
Excited by gritty process optimization and large-scale execution — you thrive on making complex operations faster, cleaner, and more reliable.

Responsibilities

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.
Diagnose bottlenecks in real time — re-sequence workflows, refine instructions, create incentive systems, and scale review processes to hit throughput targets.
Run daily “war room” syncs to stay ahead of issues before they reach the customer.
Act as the primary point of contact for researchers and program managers at frontier AI labs.
Deliver clear, consistent reporting and proactively anticipate client needs before they ask.
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.
Source, vet, onboard, train, and performance-manage domain experts across distributed workspaces.
Maintain high execution standards at every stage of production, from annotation through review through delivery.
Design motivation and performance systems — including gamification — that keep large contributor pools engaged and output high.
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.
Stay ahead of emerging practices in AI data operations and apply them before customers ask.
Champion workflow changes that reduce task completion times and improve cost efficiency.
Maintain clear, scalable documentation so that improvements survive beyond any single project.
Document onboarding scripts, quality benchmarks, contributor management frameworks, and escalation patterns.
Own your domain’s section of the SPL knowledge base.
Actively mentor the next hire — your playbook is your legacy.