Level

Turing

Strategic Project Lead, Software Engineering

Turing is hiring a Strategic Project Lead, Software Engineering in New York, San Francisco and Seattle, United States. It pays $120k-$200k a year and Level rates it ; you can apply on Turing.

Level's read on this job

  • Level rates this role Works on AI ●●●○: the daily work is on AI products, without building the model.
  • The posting mentions AI research, operations management and data pipelines among its tools and skills.
  • Level classes it as a lead role.
  • Turing has 29 other open roles on Level.

AI in this role

Own the production system and operations for Turing's software-engineering data programs supporting AI labs.

ai-researchoperations-managementdata-pipelinesteam-leadershipprogram-management
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 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

  • Work at the frontier of AI, helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks.

  • Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS.

  • Bring frontier AI innovation to the enterprise, applying lessons learned from leading AI labs to solve real-world business challenges.

  • Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies.

  • Move at the pace of AI innovation, with the speed, ownership, and impact of a startup.

 

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.

 

How we rate this

Strategic Project Lead, Software Engineering at Turing rates 70 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI ResearchOperations ManagementData PipelinesTeam LeadershipProgram Management

Questions you could be asked

  1. Tell me about a research question you investigated. What did you find?
  2. Tell me about a project where operations management was part of your work. What did you do?
  3. Tell me about a project where data pipelines was part of your work. What did you do?
  4. Tell me about a project where team leadership was part of your work. What did you do?
  5. Tell me about a project where program management was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: AI Research, Operations Management, Data Pipelines, Team Leadership, and Program Management. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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