Product Lead Engineer (AI Systems / 0->1)
New York | In-Person (4–5 days/week)
$150K–$300K + Equity
The Opportunity
We’re working with a venture-backed team building AI-native infrastructure for one of the most complex, high-friction systems in the U.S.: healthcare operations.
This is not incremental optimization.
They’re rebuilding core workflows — claims, billing, clinical documentation — as end-to-end automated systems powered by LLMs, structured data pipelines, and human-in-the-loop feedback loops.
The problems are messy:
- inconsistent inputs
- adversarial edge cases (payer rules, denials, compliance)
- real-world financial impact
The upside is equally large:
- direct control over revenue flow
- ability to reshape cost structures in a $100B+ market
- systems that compound in performance over time
The team includes engineers from top AI, quant, and systems backgrounds, and they’re scaling aggressively post-Series A.
The Role
They’re hiring a Product Lead Engineer to own a full problem space — not a feature, not a service — a domain.
This is a hybrid role by design:
- Product ownership (defining what to build and why)
- Systems thinking (designing how it works end-to-end)
- Execution (shipping and iterating quickly)
You’ll take a workflow like:
clinician input -> structured data -> payer interaction -> cash collection
…and turn it into a reliable, automated system with measurable performance.
What You’ll Actually Do
Own a Domain End-to-End
- Define system boundaries, inputs/outputs, and failure modes
- Translate ambiguous workflows into structured, testable systems
- Ship iteratively with tight feedback loops
Design AI + Systems Together
- Decide when to use models vs rules vs hybrid approaches
- Build feedback loops (human-in-the-loop, retraining signals, error correction)
- Optimize for accuracy, latency, and economic impact — not just functionality
Operate on Real Metrics
- Instrument systems around:
- claim success rates
- denial reduction
- time-to-payment
- Debug edge cases and continuously improve system performance
What They’re Looking For
Core Profile
- Strong technical background (CS, math, physics, or similar)
- Experience building 0->1 systems with real-world constraints
- Comfortable reasoning about:
- distributed systems
- data pipelines
- model behavior + failure modes
High-Rigor Signals
- Background in quant, top-tier engineering teams, or highly technical startups
- Evidence of fast learning and high slope (rapid progression, outsized ownership)
- Ability to break down ambiguous problems into structured solutions
Mindset
- You optimize for correctness and outcomes, not just shipping fast
- You’re comfortable operating without clear specs
- You care about building systems that improve over time
Why This Role
- Real technical depth — not CRUD apps, but complex, high-stakes systems
- Full ownership — you define and build, not just execute
- Tight feedback loops — your work directly impacts revenue + operations
- AI-native from first principles — not retrofitting legacy systems
- Clear trajectory -> Director-level ownership of a domain
Who This Resonates With
- Ex-quants or engineers who want to apply rigor to real-world systems
- Builders who’ve done 0->1 and want more ownership
- People who enjoy messy, adversarial problem spaces
- Engineers who think in systems, not endpoints
