Product Engineer

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