Engineering Lead, Verticalized AI
AI in this role
Location: Remote / Hybrid (NYC/SF)
Position Type: Full-time / Contract (Flexible)
About the Role
We're hiring an Engineering Lead to own Chariot's AI: the systems that originate cases and that find, for each person, the claims they're owed. This is the AI core of the product, and you'll build the team, fullstack and ML, around it.
We're building a verticalized factory for claims. AI is what lets it scale: it lets us originate cases and match people to them continuously, with humans reviewing where judgment matters, so everyday people recover what they're owed.
This role is perfect for someone who's analytical, creative, and execution-driven. You thrive in fast-paced environments, love rapid experimentation, and know how to take an idea from prototype to a production system the rest of the team can build on.
Bonus: if you've worked in a regulated industry such as legal, healthcare, or finance.
About Chariot Claims
Chariot Claims is an AI claims agent that helps consumers get paid for every corporate violation impacting them: data breaches, privacy violations, antitrust, and more. We've processed 65,000+ claims, with projected consumer recovery of $75M+.
Chariot builds technology to challenge the status quo. We take bold swings at the system to ascertain truth from stories, even if that means drawing legal/PR fire from established actors who are territorial and profit from the status quo.
As operators, we pride ourselves on our ethics, resilience, and humanity. With each turn, we emerge smarter, better prepared, and with more insiders who share our mission to build a better system. We're already working with 20+ law firms, including several among the five most active complex litigation firms in the country.
What You'll Own
Own the agent systems that source and assemble cases, with human-in-the-loop review that raises quality and throughput.
Own the systems that match people to the claims they're owed, from sources they've explicitly permissioned, with consent and privacy as first principles.
Build the evaluation and quality infrastructure that keeps agent behavior measurable and reliable in production.
Own the cost and performance of AI at scale as an engineering target.
Contribute to shared platform foundations that make the whole team faster, including internal tooling, MCPs, and agent harnesses.
Recruit, vet, and grow the engineers, fullstack and ML, on this team.
How You'll Work (AI-Native)
AI-native, not AI-assisted. You build with coding harnesses and agents in the loop every day, and you push the frontier of what they can do for the team.
You treat internal tooling as product. You build the MCPs, harnesses, evals, and human interfaces that make the whole team faster, for internal ops and external users alike.
You make product calls autonomously. You've shipped products end-to-end and can decide what to build and why without waiting to be told, then defend it with evidence.
Autonomous and collaborative. You run your vertical like an owner and still pull in the same direction as the rest of the company.
Who You Are
Experience building and shipping production software, at staff-level technical scope.
A track record of shipping products end-to-end: you've owned the outcome, not just the code.
Strong product instincts and a bias toward action; comfortable making informed product decisions on your own.
Early-stage experience (Series A or earlier).
You've built and grown a team, or are clearly ready to; you'll be hiring and leading from day one.
Bonus: experience taking LLM agent systems to production, including human-in-the-loop review and evaluation; comfort owning both the ML and the product surface around it.
Stack: Python and Go, modern LLM / agent tooling, AWS.
Why Join Now
Massive greenfield to own the AI core of the factory: the systems that originate cases and find people the claims they're owed. Help scale holding corporations accountable (in an era of unchecked corporate greed and weakening regulatory oversight).
Competitive salary, equity, and benefits package in a fast-growing, high-impact startup.
Collaborative, smart, humble, mission-driven, high-vibes team that loves music.
2-4 off-sites per year in beautiful, cultural destinations; expenses covered for you and a +1.
How we score this
Engineering Lead, Verticalized AI at Chariot Claims scores 85 out of 100 for how much of the daily work is AI. That makes it AI Level 4 of 4 (Builds AI). The level is about AI in the job, not seniority.
AI Level 4. Building AI systems is the job itself: without AI, the role would not exist.
- AI Level 480 to 100
- AI Level 360 to 79
- AI Level 240 to 59
- AI Level 10 to 39
Bands come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: AI Agents. 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.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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