Founding Strategic Project Lead
Salary: $180k - $225k base + Founding Employee Equity + bonus
Location: New York City (preferred, flexible for the right candidate)
Morpheus are partnered with a VC-backed startup building the evaluation and training infrastructure for autonomous healthcare AI. They create high-fidelity RL training gyms and environments that improve frontier models and agents on real clinical, operational, and administrative workflows, using long-horizon evaluations, expert feedback, verifier-driven tasks, RL environment design, and real-world healthcare data. The business is commercially live, connected with frontier labs, and hiring to meet immediate delivery needs.
They are looking for an exceptional Founding Strategic Project Lead to own lab-facing delivery and expansion across existing frontier lab relationships, turning early engagements into shipped RL environments, gym artifacts, and post-training datasets that convert into larger contracts. This role suits someone with extremely high agency who has shipped complex technical projects directly to frontier labs, understands RL environment design and post-training data pipelines, and can operate under ambiguity in a small, fast-moving startup while holding a high quality bar.
What You'll Do:
- Own end-to-end execution across lab engagements: scoping, timelines, milestones, risks, and delivery of RL environments and post-training data artifacts
- Be customer-facing with frontier lab stakeholders: run cadence, align on RL environment specs and acceptance criteria, and communicate progress clearly
- Translate ambiguous lab asks into concrete workstreams: RL environment/gym spec, reward and verifier design, task library plan, QC plan, and delivery roadmap
- Drive "quick-hit" pilots: deliver small, high-quality sample RL environments or post-training data slices early to prove fit and de-risk full delivery
- Coordinate cross-functionally across ML/RL researchers, environment engineers, and clinicians/domain experts to ship complex, multi-step projects on time and at frontier-lab quality
- Build and enforce delivery quality systems for RL environments and datasets: definition-of-done checklists, verifier/reward QC gates, auditability, versioning, and postmortems
- Help convert successful deliveries into expanded scopes and longer-term contracts with frontier labs
What They're Looking For:
- Demonstrated experience delivering complex technical projects directly to frontier AI labs (as a vendor, partner, or embedded team), ideally involving RL environments, evals, or post-training deliverables
- Working understanding of RL environments: reward design, verifiers, environment/task construction, and how these feed into model training and evaluation
- Hands-on familiarity with post-training data pipelines: data/annotation workflows, RLHF/RLAIF-style processes, or similar
- Exceptional ability to turn ambiguity into structure: clear goals, tight scope, crisp milestones, and measurable success criteria on multi-workstream projects
- Strong written communication, producing specs, updates, and summaries that reduce confusion and accelerate decisions with technical stakeholders
- High agency and comfort operating in a small startup with limited process, resources, or precedent to lean on
- Ability to work with technical teams (RL/ML researchers, engineers) and domain experts while making high-judgment tradeoffs on scope, quality, and timelines
- Experience in regulated domains (healthcare, fintech, security) where QA/QC and auditability matter is a plus
- Experience converting early customer demand into expanded contracts
Why Join
This is a chance to sit at the center of one of the most important bottlenecks in AI: making autonomous agents reliable in real world, high-stakes domains through better RL environments and post-training data. You'll help translate frontier lab demand into shipped artifacts that directly improve model performance, and build the delivery engine that lets the business scale.
Open to exceptional candidates across a range of seniority levels.