Qualifications: You’ve owned real systems 0→1 or 1→N, not just tickets., You think in events, state, and invariants, not just CRUD endpoints., You’re either already LLM-fluent or a strong systems/backend engineer who can get dangerous fast., You care about velocity and correctness—moving quickly while keeping things understandable and robust., You like small, high-candor teams and direct feedback., You want to see your work go live in production weekly, not sit on a roadmap., Experience with Node.js / TypeScript, Postgres, or Supabase., Experience with LLMs, agents, tool-calling, or RAG., Experience in healthcare, fintech, or other regulated / high-stakes domains., Experience in early-stage startups or founding teams.
Minimum Requirements: Comfort working in a fast-moving startup environment with high autonomy, ambiguity, and end-to-end ownership of projects.,Strong communication skills – able to partner with founders, FDEs and investigators, explain technical trade-offs, and turn messy requirements into robust systems.
Screening Questions: This step is completely optional. If you’d like, record a short 2–3 minute video introducing yourself and your experience — or share a recording of your interview with the recruiter if that’s easier. You can upload the link via Loom or Google Drive. This just helps us get to know you better, but there’s no pressure if you’d prefer to skip it.,If available, please share links to your LinkedIn, GitHub, Twitter/X, portfolio, personal website, demos, writing, talks, or other proof (tweets, papers, etc.), or any recent projects you’ve worked on. This helps provide more context about your work.,Share an example of shipping in high ambiguity and how you decided what to build first.,Describe any experience building or integrating LLM / AI-powered features into production.,Why do you want to work at Legion Health?
Company Information
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Additional Information
Interview Process: Overview of our hiring process:Systems/portfolio deep dive (45 min) – walk through 1–2 systems you’ve shipped; architecture, tradeoffs, failure modes.Practical work trial (1.5 hours) – short, realistic backend/LLM-systems exercise. No LeetCode, no puzzles.Final onsite (1.5 hours) – meet the team, pair on a real issue, and talk through how you’d own a domain., Systems/portfolio deep dive (45 min) – walk through 1–2 systems you’ve shipped; architecture, tradeoffs, failure modes., Practical work trial (1.5 hours) – short, realistic backend/LLM-systems exercise. No LeetCode, no puzzles., Final onsite (1.5 hours) – meet the team, pair on a real issue, and talk through how you’d own a domain., Estimated time-to-hire: 7–10 days, Start date: ASAP
Day to day: Own our event-driven backend – Architect and scale our Node.js / TypeScript / Supabase (Postgres) / AWS stack. Design schemas, invariants, and workflows that encode how psychiatric care actually operates. Turn messy real-world processes into clean state machines and event streams., Build real LLM agents as coworkers – Implement tool use, retries, memory, and safety rails. Design action schemas and evaluation loops so agents can run reliably in production. Work on orchestration, context management, and multi-step workflows., Shape human + AI ops UX – Build internal tools used by both humans and agents. Make it trivial to see “what happened, why, and what should happen next” in any patient journey., Define world-state & simulation – Model the canonical state of a patient’s journey across time. Power alerting, routing, and decision-making from that live simulation., Own data, safety & compliance – Engineer HIPAA-compliant pipelines for transcripts, events, and EHR data. Ensure PHI access, agent actions, and human overrides are all auditable., Drive architecture & strategy – Work directly with me to debate tradeoffs, define new primitives, and decide what we build next.
Team: Engineers at Legion Health typically report to a Head of Engineering or CTO-level technical leader, reflecting a flat and collaborative reporting structure., The engineering team works closely with Product leadership, aligning on priorities, roadmap decisions, and feature delivery., Cross-functional collaboration is expected with:Clinical teams to ensure that technical solutions meet clinician and patient needs.Design and Product partners to iterate on workflows and user experience., Clinical teams to ensure that technical solutions meet clinician and patient needs., Design and Product partners to iterate on workflows and user experience., Engineers have high ownership, meaning they frequently partner directly with leadership on implementation choices, system design, and iteration., The organization operates with a startup mindset, where communication is direct, feedback loops are short, and individual contributors can influence broader technical and product strategy., Team size (engineering): 1 CTO + 1 technical founder + 2 cracked founding engineers
Growth: Growth Opportunities: As we scale, we envision our top performers from our founding team stepping into expanded roles and joining our executive leadership team., Impact: Work on something that truly matters to millions of patients and families., Autonomy and Learning: Ownership over your projects with the freedom to set priorities, shape processes, and drive outcomes end-to-end.
Ideal Candidate Profile: Human-facing ship-fast builder: UI/UX, internal ops surfaces, fast iteration, modern dev tooling., Distributed / async systems builder: background jobs, scheduled workflows, infra primitives., Proven production shipping in our core stack (last 3–5 years): shipped a real production product using TypeScript, Next.js, and Postgres (Supabase and Vercel adjacency are strong pluses). Not acceptable: “skills section says Next.js,” side projects with 0–1 users, or frontend Next.js with backend in Python/Django., Real backend/systems competence: schema and relational data modeling, stateful workflows, transitions and invariants, APIs that reflect real constraints (not CRUD hand-waving)., State / invariants mindset: can translate messy workflows into explicit states, valid transitions, and invariants that cannot be violated (e.g., cannot double-book a provider slot; cancelled appointment cannot trigger upcoming comms; agents cannot spam or duplicate)., Can build both surfaces: independently deliver human-facing UIs (ops dashboards, admin tools) and background orchestration (scheduled jobs, workflows, agent runs)., Early-stage + SF requirement: excited about early-stage execution, not remote-only (SF or willing to relocate), and wants to execute as an IC (not transitioning into management)., Uses modern AI coding tools: heavy, habitual user of AI-native dev tools (Claude Code, Codex, Cursor, Cline, etc.) with real workflow opinions and examples of how these tools accelerate shipping without slop., Research/academic/healthcare-ML without production systems: papers/datasets/classifiers with no real product shipping; Python-first ML pipelines with no systems ownership., Frontend-only / UI-only: shallow API language without data model or state thinking., No real proof of build: GitHub mostly coding challenges/school/take-homes; <100 contributions in the past year; one-commit repos; low-effort links; no demos/repos/LinkedIn or inspectable output., Stack misalignment in practice: no recent production shipping in Next.js + TypeScript + Postgres (last 3–5 years)., Planner/executive returning to IC: long time since writing production code; primarily managed orgs/roadmaps/outsource teams., Long indie-hacker gap with minimal output: 6–12 months ok with strong story; 3–4 years with <$1k/mo traction is a strong negative signal., Short founder stint (~6 months): must explain clearly., Low GitHub contributions: sparse activity (e.g., ~100–500 in past year) must be explained with alternative proof-of-build (private repos, company policy, etc.)., Enterprise background: depends on which teams and how they shipped; fast-moving teams can be good, process-heavy orgs usually misaligned., Hackathon wins without real contribution: must verify via commit history., Production LLM app engineering: ownership of prompting, context management, tool-calling patterns, evaluation/monitoring, and debugging real failure modes in production., 10k–100k user feature ownership: personally shipped and iterated features used by 10k–100k users recently., Performance fundamentals for chunky data: batching/pagination, relational query design, real-world performance debugging for dashboards and joins., Lightweight CI/testing that increases velocity: pragmatic Playwright smoke tests, GitHub Actions, focused tests for critical flows., 100+ GitHub stars: strong signal; 1,000+ stars is an extremely strong signal., Multiple hackathon / coding competition wins: as a meaningful contributor., Counts: production product shipped and maintained over time; evidence of iteration across time; real users and operational pressure; adoption or revenue is a strong plus., Does NOT count: one-off demos, school projects, take-homes, coding challenges, side projects with only the candidate as a user., High-yield archetypes: early-stage product engineers shipping TS/Next/Postgres with real users; technical founders/CTOs (≤4 engineers) still hands-on; engineers who built ops dashboards/control planes for stateful systems; engineers who shipped LLM product features in JS/TS stacks; visibly AI-native builders with real shipped output., Low-yield archetypes: healthcare ML researchers; frontend-only specialists; infra/SRE-only without product ownership; exec/VP/CTO profiles without recent hands-on shipping., Experience level: level does not matter if you are exceptional., Thrives in an early-stage, high-velocity startup environment., Comfortable with ambiguity, rapid iteration, and full ownership of systems., Strong at end-to-end system design and building long-lived services., Builds reliable workflows and understands stateful systems., Experienced with backend engineering in production environments., Comfortable building APIs, working with databases, and shipping to production., Thinks about latency, observability, error handling, and operational reliability., Learns quickly and adapts as priorities shift., Communicates technical decisions clearly and collaborates with Product, Clinical, and Design., Interested in or experienced with AI-augmented systems and LLM-powered workflows., Values security, privacy, and compliance when working with sensitive patient data., Motivated by mission-driven work with real-world impact in healthcare.
