AI Engineering Lead (AIOS)

Join AIOS, a leading Applied AI team in the healthcare industry. As the AI Engineering Lead, you will build the AIOS Agent SDK, a foundation for world-class agents across the company. You will also lead the development of an AI clinical decision-support system and be responsible for agent architecture, model strategy, evaluations, AI reliability, technical safety, provider relationships, and the shared runtime. This is a senior technical leadership role, reporting directly to the VP of Engineering.

Missions

• Construire le SDK de l'agent AIOS et en faire la base pour des agents de classe mondiale.
• Séparer les fondations réutilisables de la logique de support client et les transformer en une plateforme interne fortement opinionnée.
• Être le responsable de l'architecture des agents, de la stratégie des modèles, des évaluations, de la fiabilité de l'IA, de la sécurité technique.

Profil recherché

- Model Judgement: You understand the capabilities and limitations of current frontier and open-weight models. You know when the model is the problem and when the real problem is context, tools, data, orchestration, or evaluation

- Open-Weight Models: You have enough technical depth to lead the fine-tuning and AIOS-controlled deployment of open-weight models when the evidence supports doing so. Prior production deployment is not required

- Agent Architecture: You can reason deeply about harnesses, orchestration, context construction, retrieval, memory, state, tool design, structured workflows, and error recovery

- Consequential Actions: You know how to let an agent act safely. You have strong judgment around authorization, validation, idempotency, state transitions, auditability, recovery, and escalation

- Technical Authority: Strong engineers trust your judgment. You can make difficult decisions, explain the trade-offs clearly, and push back without hesitation when a proposed approach is unsound

- Communication: You can explain difficult technical ideas to engineers, product leaders, clinicians, and executives without flattening the important details

- Experience: You have 8+ years of software engineering experience and remain an active production contributor

- Leadership: You have successfully led and managed a small technical engineering team. You set a clear direction, raise the quality bar, develop strong engineers, and address underperformance

- Independence: You create clarity in ambiguous environments and make high-quality decisions without hand-holding

- Software Engineering: You have strong systems-engineering fundamentals. You can reason about APIs, distributed systems, concurrency, queues, databases, observability, failure modes, and production reliability

- Education: You have at least a bachelor’s degree in Computer Science, Machine Learning, or a closely related technical field

- Ownership: When quality drops, costs spike, tools fail, or providers degrade, you take responsibility for reaching the outcome rather than identifying whose component was technically at fault

- Evals: You’ve built or meaningfully owned evaluation systems for probabilistic products. You understand dataset construction, evaluator design, simulations, regression detection, noisy metrics, and the relationship between offline performance and production outcomes

- Production Agents: You have personally built and shipped an exceptional agentic system used by real customers. It did more than answer questions: it reasoned across multiple steps, used tools, changed state, and operated under real production constraints

- Customer Agents: You’ve built high-volume customer-service, commerce, or transactional agents operating across complex, multi-step customer journeys

- Agent Platforms: You’ve built runtimes, SDKs, harnesses, tool layers, evaluation platforms, or shared AI infrastructure used by other engineers

- High-Stakes Systems: You’ve worked on healthcare, financial, insurance, or other systems where correctness, traceability, and careful rollout matter

- Model Adaptation: You’ve fine-tuned, distilled, evaluated, or deployed an open-weight model for a specific production workflow

- Long-Term Memory: You’ve built durable memory, context compression, personalization, or agents operating across sessions and extended periods

- Real-Time Systems: You’ve worked on voice agents, streaming systems, or other latency-sensitive AI experiences

- Research Fluency: You can translate relevant research into reliable production systems without confusing novelty with progress

- Talent: You have a strong nose for exceptional AI engineers and know how to create an environment in which they do their best work

- Provider Relationships: You’ve worked directly with frontier model providers on evaluations, technical issues, capacity, pricing, or early access

- Figure It Out: You can move from debugging a production trace, to redesigning an eval, to reviewing an agent abstraction, to handling a provider incident