Head of Engineering

Leap is one of the fastest-growing benefits solutions and a category-defining pioneer in employer specialty pharmacy. We are reshaping how life-changing therapies are delivered and financed, ensuring patients get the treatment they need while employers finally get a fair deal.

Specialty drugs and infusions represent nearly 10% of all healthcare spend and are the fastest-growing cost category for employers. Leap tackles this challenge with a novel approach: eliminating hidden markups, expanding access to high-quality infusion providers, and bringing clarity and fairness to how therapies are priced and paid for.

We’re proud to partner with numerous Fortune 500 companies and leading TPAs. Each patient we serve creates immediate ROI: lower costs, improved access, and better care. Join us as we redefine what’s possible in specialty care.

Leap is entering a phase where engineering becomes a core source of company leverage.

This is Leap’s first engineering leadership role. You will take a small, high-caliber founding team and build the engineering organization, technical foundation, and product velocity required to turn Leap’s data advantage into durable product advantage.

Leap sits at a rare intersection in healthcare: employer membership, claims data, drug-level economics, clinical workflows, provider networks, procurement pathways, and direct patient and prescriber touchpoints. Most healthcare companies see only fragments of this picture. Leap is connecting the full journey from patient identification to therapy access to measurable savings — creating products that traditional PBMs, specialty pharmacies, and data aggregators are not built to deliver.

The work is technically demanding and central to the business: production AI, data infrastructure, workflow orchestration, and applied healthcare systems on messy, lagging, real-world data. Correctness matters. A flawed drug mapping, attribution model, routing decision, or intervention signal can affect patients, providers, employers, and Leap’s credibility.

This is a rare chance to build an engineering org from the ground up in a market where the hardest problems are still unsolved — and where the data to solve them is finally coming together in one place. You will report directly to Leap’s President and co-founder.

• Own hiring end to end — set ambitious targets, build the pipeline, and close engineers who clear a high bar

• Grow the team from its founding core into a high-performing organization

• Define what great looks like: the bar, the interview loop, and the standard the team is held to

• Build the structure and culture a fast-moving team needs as it scales

• Own and sequence the technical roadmap, making the prioritization and build-vs-buy calls that hold up as Leap scales

• Build the cross-benefit drug identity layer that resolves one specialty therapy across NDC and HCPCS/J-codes as biosimilars multiply

• Ship causal savings attribution that proves impact rather than projecting it — ROI an employer can defend in an audit

• Build the intervention engine and AI workflows — at-risk prediction, prior authorization, formulary and site-of-care optimization — that turn analysis into action

• Set the standard for how Leap builds: AI-native, fast, and correct by default

• Make the foundational data and architecture decisions the rest of the roadmap depends on

• Unblock the team, own the hard technical calls, and dive in when it matters

• You've built and scaled an engineering team from a handful of people into a high-performing org — you've owned hiring, set the bar, and closed exceptional engineers.

• You're AI-native: you've shipped AI systems in production with LLMs and agents, and you lead teams that do the same.

• You've operated at a high bar at organizations known for exceptional engineering — your track record speaks for itself.

• You're a lean player-coach who leads first but keeps the technical depth to make sharp architecture calls and own the roadmap.

• You've shipped production systems on messy, real-world data — not clean benchmarks — and you build for correctness when being wrong has consequences.

• Healthcare, HIPAA, claims, or clinical-data experience

• Production ML or data-intensive systems: causal inference, prediction, or large-scale pipelines

• Leap's stack: TypeScript, React, Node/Python, PostgreSQL, BigQuery, GCP, or LLM/agent systems

• Early-stage experience as a founder, founding engineer, or first engineering leader