About Deeptune

Deeptune builds training gyms for AI agents: high-fidelity simulation environments where AI learns to do real work through reinforcement learning. We work with the leading AI labs to help them train the next generation of agentic models, and our environments have already contributed to recent breakthroughs in computer use, code generation, and multi-step task completion.

We're a ~20-person team of engineers and operators from Anthropic, Scale AI, Palantir, Hebbia, Glean, and Retool, based in-person in New York City. We recently raised a $43M Series A led by Andreessen Horowitz, with participation from 776, Abstract Ventures, and Inspired Capital.

If you want to work on the hardest problems at the intersection of reinforcement learning and real-world AI deployment, we'd love to hear from you.

Read more: Fortune exclusive on our Series A | a16z: Why we're investing in Deeptune

About the Role

We're looking for a Strategic Projects Lead to own and scale the data operations that power frontier AI training. This is a high-ownership role for someone with strong agency, comfortable making decisions with incomplete information, and capable of building processes that hold up under pressure.

As an SPL, you will own projects end-to-end, from scoping through final delivery. You'll be responsible for managing a distributed team, forecasting timelines, meeting throughput targets, and maintaining a high bar for everything that ships. You'll face ambiguous problems and are expected to solve them in ways that scale across the org. Your work will directly shape frontier models and how they're deployed.

What you’ll do

• Own delivery end-to-end: You’re accountable for data delivery end to end, including deadlines, throughput, and quality

• Solve ambiguous problems: When you find a broken or inefficient process, you will build a scalable solution that the whole org will benefit from

• Build and lead contributor teams: Recruit, train, and manage a distributed workforce; turn new hires into high performers

• Partner cross-functionally: Work closely with our engineers to scope technical requirements, flag blockers, and ship improvements to our pipeline infrastructure

• Drive continuous improvement: Identify bottlenecks, inefficiencies, and quality gaps, and then build the solutions

SPLs are obsessed with both quality and velocity. You’ll combine analytical rigor, quantitative thinking, strong judgment, and creative problem-solving to move data efficiently through complex systems – often with imperfect information.

About You

• You're a self-directed operator who takes ownership without being asked.

• You'd rather build a system than do the same task twice.

• You're motivated by the chance to shape how frontier AI gets built.

• You are motivated by ownership, impact, and building frontier tech.

• Your work is a craft you want to master.

• You thrive in ambiguity and love hard problems.

• You appreciate diverse perspectives and uncommon ideas.

• You are excited about building in-person, 5 days a week, from our office in Union Square NYC.

Ideally, you have:

• 2+ years of experience in high-intensity, results-driven environments (startups, consulting, banking, or similar)

• A track record of owning outcomes and delivering under pressure

• Strong problem-solving skills, a bias toward action, and a data-driven approach to improving systems and processes

• Clear communication skills, especially with technical teams

• The ability to move between hands-on execution and systems-level thinking

• An entrepreneurial mindset to quickly build from 0 to 1

• Genuine interest in AI / ML systems

Bonus points if you have:

• Experience in data labeling, AI data operations, or similar human-in-the-loop workflows

• Experience leading or managing teams (even informally)

• Familiarity with SQL, Python, or similar tools

You should apply if:

• You thrive in ambiguity and figure things out fast

• You want real ownership and accountability for outcomes

• You're excited to build scalable systems, not just complete one-off tasks

Interview process

1. You submit an application that will be reviewed by a human

2. Intro call with an Ops team member

3. Live Case Study with another Ops team member

4. Join us onsite for ~4hrs, have lunch with the team, take on our final round challenge, and meet Tim, our founder

5. Offer decision within 24 - 48 hrs