Job description

About Turing

Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises looking to deploy advanced AI systems. Turing accelerates frontier research with high-quality data, specialized talent, and training pipelines that advance thinking, reasoning, coding, multimodality, and STEM. For enterprises, Turing builds proprietary intelligence systems that integrate AI into mission-critical workflows, unlock transformative outcomes, and drive lasting competitive advantage.

Recognized by Forbes, The Information, and Fast Company among the world’s top innovators, Turing’s leadership team includes AI technologists from Meta, Google, Microsoft, Apple, Amazon, McKinsey, Bain, Stanford, Caltech, and MIT. 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 Do

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.
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.

2) 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.
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.

3) Large-scale coordination — orchestrate the work of 100–1,000+ contributors

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.

4) 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.

5) Process innovation — make the operation faster, better, and cheaper each cycle

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.

6) Playbook building — codify what works so future SPLs scale faster than you did

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.

Who We’re Looking For

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.

What Success Looks Like

30 days: First project delivered end-to-end with no quality escapes reaching the customer. Reporting cadence established and trusted by the lab. Contributor onboarding playbook v1 published. You know the names of every researcher on your accounts.

60 days: 300+ active contributors across concurrent workstreams, all executing to standard. At least one customer has proactively expanded scope based on delivery quality. Quality framework codified and in daily use by your team.

180 days: $5M+ in active project revenue under your management. A second SPL is ramping off your playbook. You spend more time multiplying through others than operating as a solo contributor.

Why Turing

Work directly with the world’s leading AI labs at the cutting edge of post-training, evaluation, and agentic AI research.
Real impact on the path to AGI: the data you deliver will directly influence how frontier models are trained and evaluated.
High ownership and influence. You will shape how Turing delivers at scale, with direct visibility to senior leadership.
Direct-to-research customers. You will spend your time partnering with the people building the future of AI, not coordinating with procurement.

How to Apply

Send a CV and a short note on a project you managed end-to-end — ideally something that required coordinating a large team, managing a demanding client, or solving a hard quality problem under time pressure — to recruiting@turing.com. We read every submission.

Compensation

SPL:

Base Salary: $120K-$200K
Total Target Compensation: $195K-$300K (includes salary, variable, and equity)

Senior SPL:

Base Salary: $150K-$280K
Total Target Compensation: $300K-$500K (includes salary, variable, and equity)

Values

We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value.
We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection
We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.

Advantages of joining Turing

Amazing work culture (Super collaborative & supportive work environment; 5 days a week)
Awesome colleagues (Surround yourself with top talent from Meta, Google, LinkedIn etc. as well as people with deep startup experience)
Competitive compensation

Don’t meet every single requirement? Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

For applicants from the European Union, please review Turing's GDPR notice here.

Strategic Project Lead, Software Engineering at turing | AI Job Board