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About This Role
About Legion Health
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At Legion Health, we believe everyone deserves fast, affordable, and world\-class health care, and we’re developing the AI infrastructure to deliver it ourselves at world scale.
Legion is building autonomous medical care (the AI doctor), starting with psychiatry. Our AI\-native care\-delivery platform currently automates 95% of the administrative work required for us to deliver direct patient care. We also recently became the first company ever to receive regulatory authorization to let AI prescribe psychiatric medications, allowing us to not only collapse health care’s admin costs but also its clinical labor costs—shifting this industry’s economics from humans to tokens.
Our technical moat is hard to copy—we combine rich data, production AI, doctors in the loop, and end\-to\-end care operations to deliver measurably better care to a clinically complex patient population. This has helped us produce 25K\+ total visits in just 2 years, all while holding ops costs flat and achieving industry\-leading patient NPS and retention. While the last generation of healthcare startups made the existing system incrementally more efficient, Legion is rebuilding full\-stack care delivery from first principles. Our vision? A 10X better patient experience that’s higher quality, less expensive, and more scalable than ever before. And a 10X better clinician experience that frees our providers to focus on what matters most: caring for patients.
Legion Health is backed by Y Combinator, leading venture capital firms, and founders from Function Health, Modern Health, Everly Health, Trusted Health, Clipboard Health, PatientPing, Sesame Care, Faire, EasyPost, and fuboTV.
Join us as we build the future of health care: faster, higher\-quality, and more affordable, powered by doctors and AI working together.
Role Logistics
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- Job Type: Full\-Time
- Role Type: AI\-Native Associate / GTM / Executive Operations / Business Operations / Special Projects
- Ideal Experience Level: 1\-5 years
- Location: In\-person in San Francisco
- US Visa Sponsorship: Yes
The Opportunity
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Legion is hiring an AI\-Native GTM \& Business Operations Lead: an unusually sharp early\-career technical operator who will work directly with leadership to solve high\-leverage problems across growth, revenue, finance, hiring, culture, legal, investor work, operations, and internal AI systems.
This is not an executive assistant role. It is not a traditional business operations role. And it is not a junior Chief of Staff role with a better title. It is a new kind of role for the AI\-native era: a technical, high\-agency generalist embedded with senior leaders to make the company dramatically faster.
You will sit close to leadership, see the company’s most important problems early, and be expected to build solutions fast. One day you might prototype an internal AI tool that saves the operations team 30 hours per week. The next day you might analyze patient reactivation, build a GTM model, improve a hiring process, automate investor reporting, draft a strategy memo, or create a workflow that helps a non\-technical team specify exactly what they need before engineering gets involved.
The center of gravity is GTM and company leverage. You should constantly be asking: where are we losing patients, revenue, speed, quality, or focus, and what can I build, automate, analyze, or fix this week to move the company forward?
The best candidates are likely 0\-3 years out of school, with a computer science, engineering, data science, operations research, financial engineering, applied math, statistics, economics, or similarly rigorous background. You may have early experience in consulting, banking, finance, startups, product, data, or engineering. What matters most is raw intelligence, speed, taste, grit, communication, AI fluency, and demonstrated ability to make things.
This is a career\-accelerating role for someone who wants unusual exposure very early. You will be in the room for meaningful company decisions, work directly with senior leaders, get tough deadlines, and learn by doing real work that matters.
Responsibilities and Deliverables
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- Work directly with founders and leadership on high\-priority projects across GTM, growth, revenue, business operations, regulatory, finance, hiring, culture, legal, investor relations, product, and internal systems.
- Drive go\-to\-market and revenue projects across new patient acquisition, conversion, retention, reactivation, referrals, lifecycle, pricing, patient experience, and funnel optimization.
- Build AI\-native tools, workflows, dashboards, scripts, agents, prototypes, and automations that help teams operate faster and better.
- Rapidly prototype solutions for non\-technical teams so they can clarify what they want before full engineering resources are used.
- Identify bottlenecks across the company, diagnose root causes, and build fast solutions: models, dashboards, workflows, memos, trackers, experiments, or internal products.
- Use AI coding and building tools like Codex, Claude Code, Cursor, Replit, v0, Lovable, ChatGPT, Claude, Perplexity, APIs, scripts, and no\-code tools to move faster than a normal early\-career operator.
- Build internal systems for leadership: synthetic advisory boards, investor update workflows, meeting\-prep agents, board\-material review systems, recruiting trackers, finance dashboards, GTM dashboards, and executive decision\-support tools.
- Support strategic finance work: revenue models, CAC/payback analysis, hiring plans, budget scenarios, growth forecasts, investor updates, board materials, and operating reviews.
- Help build Legion’s talent machine: sourcing systems, role scorecards, interview loops, candidate evaluation processes, onboarding systems, culture rituals, and performance operating rhythms.
- Partner with growth, product, data, clinical operations, and leadership to analyze the patient journey: intake, booking, first visit, follow\-up, renewals, retention, churn, and reactivation.
- Support legal, compliance, regulatory and finance workflows with maturity and attention to detail, especially around healthcare, HIPAA, contracts, vendors, employment, patient communication, and AI sandboxes.
- Write crisp memos, project plans, launch docs, investor materials, candidate materials, process docs, and internal updates that create clarity quickly.
You’ll Be Successful If You…
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- Become one of the people leadership trusts with ambiguous, high\-priority problems.
- Consistently turn vague problems into shipped work: models, tools, workflows, experiments, docs, dashboards, or decisions.
- Help Legion grow revenue by improving acquisition, conversion, retention, reactivation, referrals, lifecycle, pricing, or patient experience.
- Build AI\-native systems that actually get adopted by the team and save meaningful time or improve quality.
- Prototype solutions fast enough that projects do not sit in a six\-month backlog.
- Help non\-technical teams become much better at specifying what they need from product and engineering.
- Make senior leaders meaningfully more effective by turning ideas into working systems.
- Make the company faster without making it sloppy.
- Raise the bar for how everyone at Legion uses AI tools.
- Bring order to chaos without creating bureaucracy.
- Combine extremely high IQ with extremely high EQ: sharp thinking, low ego, clear writing, good taste, and strong emotional read of people and situations.
Ideal Background and Skills
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- 0\-3 years of experience in a high\-performance environment such as consulting, investment banking, private equity, strategic finance, startup operations, product, data, engineering, or a similarly intense setting.
- Strong preference for a technical or quantitative degree: computer science, engineering, data science, operations research, financial engineering, applied math, statistics, economics, or similar.
- Demonstrated competency in making things and automating things. You have built projects, scripts, tools, automations, prototypes, dashboards, agents, websites, apps, analyses, or workflows that actually worked.
- Deep AI\-native working style. You do not just “use ChatGPT.” You use AI tools to build, analyze, automate, prototype, research, write, debug, synthesize, and move faster every day.
- Ability to build lightweight tools or prototypes using some mix of Python, JavaScript, SQL, spreadsheets, APIs, no\-code tools, AI coding tools, product prototyping tools, and data platforms.
- Strong analytical horsepower. You can build a model, interrogate assumptions, analyze a funnel, understand unit economics, and explain what matters.
- Strong GTM instincts. You care about patient growth, revenue, conversion, retention, activation, reactivation, CAC, payback, funnel dropoff, and the practical mechanics of moving numbers.
- Excellent written communication. You can write a memo, investor update, recruiting note, project spec, growth analysis, operating plan, or executive summary that is crisp and useful.
- Strong people judgment. You can work with founders, operators, clinicians, engineers, lawyers, candidates, investors, and vendors without losing context or trust.
- Comfort with sensitive information. You can handle patient, company, legal, financial, recruiting, investor, and operational information with maturity and discretion.
- High agency and low need for structure. You do not wait for someone to define the perfect project. You find leverage and go create it.
- High\-conviction interest in Legion. This role is intense, ambiguous, and close to leadership. It is best for someone who genuinely wants to be here and sees this as a chance to put their career on a much faster track.
This Role Is Not For You If…
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- You want a narrow lane with a fixed job description.
- You prefer advising over doing.
- You cannot figure what to do to move the needle and operate without much direction.
- You are not deeply curious about AI tools.
- You need engineers or analysts to answer every first\-pass technical or data question.
- You are uncomfortable with growth, revenue, or commercial pressure.
- You optimize for looking strategic instead of creating measurable leverage.
- You want prestige without intensity.
- You do not want to work closely with leadership in a high\-speed environment.
- You are not excited to work in person, close to the team, with real urgency.
Compensation \& Benefits
============================
- Salary: $110,000 – $190,000
- Early\-Stage Equity: Competitive
- Health Insurance: Medical, dental, and vision benefits
- Additional Perks: In\-person retreats, meal stipends, AI tooling budget, learning stipend, home office support
- Time Off: Flexible, unlimited vacation policy
- Work Hours: Flexible, with responsiveness around high\-priority company moments
- Work Setup: In\-person in San Francisco, 5 days/week strongly preferred
- Impact: Work directly on the highest\-leverage problems at a company trying to change health care forever.
- Autonomy and Learning: Operate across growth, finance, hiring, AI systems, investors, legal, product, culture, and operations.
- Growth Opportunities: This role can grow into larger roles across GTM, business operations, product operations, strategy, or company\-building.
How to Apply and Hiring Process
===================================
- Apply here: https://jobs.ashbyhq.com/legionhealth/839f7461\-1956\-47b9\-81a9\-a86db8a5509c/application
- Overview of our hiring process:
+ Resume \+ Application Screen
+ Founder Interview
+ Strategy \+ GTM Case Study \+ Take\-home project: audit a Legion growth or operating problem, identify the highest\-leverage opportunities, build a simple model, propose a 30\-day execution plan, and create one AI\-assisted prototype, workflow, dashboard, or automation that would help solve it.
+ Professional Reference Checks
- Estimated time\-to\-hire: 2 weeks
- Start date: ASAP
- Hiring manager: Yash Patel, CEO
Equal Employment Opportunity
================================
Legion Health is proud to be an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees, contractors, and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, genetic information, or any other protected characteristic under applicable law. We encourage applicants from all backgrounds to apply.
Salary Context
This $110K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Legion Health, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($150K) sits 19% below the category median. Disclosed range: $110K to $190K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Legion Health AI Hiring
Legion Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $190K - $190K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,258 tracked positions. That's 26% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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