Head of AI Engineering at AIOS — Remote, $200-$400k/yr + equity

AIOS (YC W20/S21)

18d•$200,000 - $400,000•Remote

About The Position

AIOS is building the world’s first full-stack AI doctor. We’re at $350M ARR, growing from $10M/yr 12 months ago, making us the world’s fastest-growing AI doctor. We serve over 150k/mo patients via Bolt Pharmacy, our profitable main UK brand. Our strategy leverages the rapid growth of GLP-1s and the overlooked $2T European healthcare market. Our master plan includes reaching $1B ARR by the end of 2026, $10B ARR by the end of 2028 by scaling across Europe, and $100B ARR by 2031 with our Full Autonomous Prescribing (FAP) system. By 2035, we aim for $1T/yr, enabling global treatment of any patient with any medication using AIOS, potentially becoming the world's first trillion-dollar healthcare company. We are a young, founder-led company, still in Day 1 with all our work ahead of us.

Requirements

8+ years of software engineering experience with active production contribution.
At least a bachelor’s degree in Computer Science, Machine Learning, or a closely related technical field.
Personally built and shipped an exceptional agentic system used by real customers, which reasoned across multiple steps, used tools, changed state, and operated under real production constraints.
Deep understanding of agent harnesses, orchestration, context construction, retrieval, memory, state, tool design, structured workflows, and error recovery.
Experience building or meaningfully owning evaluation systems for probabilistic products, including dataset construction, evaluator design, simulations, regression detection, noisy metrics, and understanding the relationship between offline performance and production outcomes.
Strong systems-engineering fundamentals, including APIs, distributed systems, concurrency, queues, databases, observability, failure modes, and production reliability.
Experience ensuring agent actions are safe through authorization, validation, idempotency, state transitions, auditability, recovery, and escalation.
Understanding of the capabilities and limitations of current frontier and open-weight models, and ability to identify if the model, context, tools, data, orchestration, or evaluation is the root problem.
Technical depth to lead the fine-tuning and AIOS-controlled deployment of open-weight models when evidence supports it.
Successfully led and managed a small technical engineering team, setting clear direction, raising quality bar, developing engineers, and addressing underperformance.
Strong technical authority, with engineers trusting judgment and ability to make difficult decisions and explain trade-offs.
Ability to explain difficult technical ideas to engineers, product leaders, clinicians, and executives.
Ability to create clarity in ambiguous environments and make high-quality decisions without hand-holding.
Still writes production code and leads from inside the work.
Takes responsibility for outcomes when quality drops, costs spike, tools fail, or providers degrade.

Nice To Haves

Experience building runtimes, SDKs, harnesses, tool layers, evaluation platforms, or shared AI infrastructure used by other engineers.
Experience building high-volume customer-service, commerce, or transactional agents operating across complex, multi-step customer journeys.
Experience working on healthcare, financial, insurance, or other systems where correctness, traceability, and careful rollout matter.
Experience fine-tuning, distilling, evaluating, or deploying an open-weight model for a specific production workflow.
Experience building durable memory, context compression, personalization, or agents operating across sessions and extended periods.
Experience working on voice agents, streaming systems, or other latency-sensitive AI experiences.
Experience working directly with frontier model providers on evaluations, technical issues, capacity, pricing, or early access.
A strong ability to identify exceptional AI engineers and create an environment where they do their best work.
Ability to translate relevant research into reliable production systems without confusing novelty with progress.
Ability to move from debugging a production trace, to redesigning an eval, to reviewing an agent abstraction, to handling a provider incident.

Responsibilities

Build the AIOS Agent SDK and make it the foundation for world-class agents across the company.
Separate reusable foundations from customer-support logic and turn them into a strongly opinionated internal platform.
Build an AI clinical decision-support system as the second major system on the SDK.
Be the DRI for agent architecture, model strategy, evals, AI reliability, technical safety, provider relationships, and the shared runtime.
Ensure the best model is used for each job based on measured quality, reliability, speed, and cost.
Lead by example as you grow the team.
Ensure the AIOS Agent SDK is running the show in production and is in exceptionally safe technical hands.
Enable product engineers to build excellent agents without recreating context, tool, safety, eval, and observability infrastructure.
Ensure agents become more capable without becoming less predictable.
Ensure major changes are supported by trustworthy evidence across quality, reliability, safety, latency, and cost.
Ensure production failures continuously strengthen evals, architecture, and models.
Ensure engineers actively seek your judgment and trust the direction you set.
Turn Jesse’s existing harness into the strongly opinionated internal platform powering Jesse, Aegis, and future AIOS agents.
Own the SDK’s architecture, reusable primitives, supported extension points, developer experience, and integration with existing infrastructure.
Become the senior technical owner of Jesse and work closely with the engineers and Clinical Product team building Aegis.
Improve Jesse and Aegis while extracting shared foundations across context, retrieval, memory, orchestration, tools, state, and escalation.
Build trustworthy benchmarks using deterministic checks, simulations, model-based graders, human judgment, and production outcomes.
Establish the path from offline evaluation to controlled production experiments.
Turn traces, poor resolutions, escalations, incidents, tool failures, and successful outcomes into better evals, stronger architecture, improved models, and permanent platform capabilities.
Make consequential agent actions safe through authorization, validation, idempotency, auditability, recovery, and human handoff.
Encode compliance, privacy, security, and regional requirements into the platform.
Own model selection, routing, fallbacks, caching, and manage ~$200k monthly model spend.
Lead data preparation, fine-tuning, evaluation, and AIOS-controlled deployment of specialized open-weight models when evidence supports it.
Own the shared runtime in production, including tracing, observability, testing, provider resilience, capacity, and incident response.
Be the senior engineering DRI when an AI system behaves unsafely, quality regresses, or the platform fails.
Set AIOS’s AI architecture and strategy in close partnership with the VP of Engineering.
Make the final call on major technical decisions, guide engineers across product pods, and remain hands-on by writing production code and personally building the most important foundations.
Build the Applied AI team to approximately five exceptional people during the first year.
Own technical relationships with leading model providers and represent AIOS externally.
Set the AIOS’s AI architecture and strategy in close partnership with the VP of Engineering.