Member of Technical Staff - Forward Deployed AI Engineer, Health

Microsoft•New York, NY

22d•$142,800 - $331,200•Onsite

About The Position

At Microsoft AI, our Health team is on a mission to help millions of people better understand and proactively manage their health and wellbeing. We are responsible for ensuring that Microsoft AI's models, products, and services are useful, trusted, and safe across diverse health journeys. Microsoft AI and Mayo Clinic have announced a strategic collaboration to develop and deploy a frontier AI model designed specifically for healthcare. The collaboration combines Mayo Clinic's clinical expertise, de-identified clinical health data, longitudinal insights, and trusted environment with Microsoft's advanced AI, cloud, engineering, and superintelligence capabilities. The model will initially be deployed within Mayo Clinic, where it can be tested, refined, and improved through real-world use. We are looking for a Forward Deployed AI Engineer to join the Microsoft AI Health team and play a leading technical role in this collaboration. This is a very senior, deeply hands-on individual-contributor position at the intersection of Applied AI, platform engineering, data engineering, and forward-deployed delivery. You will act as a technical lead for the engineering work between Microsoft AI and Mayo Clinic. You will embed closely with teams across both organizations, understand a complex environment of clinical data platforms and legacy systems, and identify the shortest path from ambiguity to working AI capabilities. You will lead through technical judgment, code, architecture, influence, and delivery rather than formal authority. The role is based in New York and will initially focus exclusively on the Mayo Clinic collaboration. Regular travel to work directly with Mayo Clinic teams will be required. What Applied AI and forward-deployed engineering mean at Microsoft AI: We turn frontier models into products people can trust with their health. We build rigorous, health-specific evaluations and use them to drive model, product, and engineering decisions. We design systems that combine models, context, retrieval, tools, enterprise data, and deterministic software to deliver reliable results in high-stakes environments. Forward-deployed engineering means working directly in the complexity of a partner organization. It requires learning unfamiliar systems quickly, building trust across a large stakeholder network, and finding pragmatic ways through technical, organizational, and operational constraints. You will not stop at recommendations or prototypes: you will build alongside partner teams and remain accountable through integration, deployment, validation, and adoption. The goal is both to deliver the Mayo Clinic collaboration and to strengthen the platform beneath it. You will turn partner-specific learning into reusable infrastructure, data capabilities, integration patterns, and paved paths that can support the frontier healthcare model and future health AI products.

Requirements

Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
Experience designing and operating distributed systems, data platforms, and integrations across large enterprise architectures and datasets.
Experience building production LLM or machine-learning systems, including context engineering, retrieval, tool use, orchestration, and evaluation.

Nice To Haves

Strong programming ability, preferably including Python, and experience with platform technologies such as Kubernetes, Databricks or Spark, infrastructure as code, and observability.
Ability to lead through influence in a complex partner environment and communicate technical decisions, progress, and risks to C-suite audiences.
Experience in a forward-deployed engineering, field engineering, customer engineering, or similarly embedded technical-delivery role.
Experience leading deployments with healthcare providers, payers, life-sciences organizations, or other large, highly regulated enterprises.
Experience with electronic health records, clinical data platforms, healthcare interoperability, or longitudinal health datasets.
Strong machine-learning or applied-science foundations, including experience adapting, evaluating, or deploying foundation models on domain-specific data.
Experience converting partner-specific solutions into reusable platforms, tooling, or integration frameworks.
Demonstrated 0-to-1 delivery: repeatedly moving from an ambiguous problem to a working production capability with measurable outcomes.
Significant software-engineering experience, including hands-on technical leadership of complex production systems and major cross-organizational initiatives.

Responsibilities

Serve as a senior technical lead for the forward-deployed engineering effort with Mayo Clinic, owning complex work from discovery and architecture through implementation, deployment, and operational adoption.
Build deep relationships across Microsoft AI and Mayo Clinic, learn Mayo's clinical and technical environment, and turn ambiguous goals into executable plans.
Design and build integrations, data pipelines, and platform capabilities across clinical data platforms, APIs, identity boundaries, and legacy enterprise systems.
Architect and evaluate production AI systems that combine frontier models, context engineering, retrieval, tool use, orchestration, data platforms, and deterministic services.
Stay deeply hands-on in code and infrastructure, solving difficult integration, data, reliability, and production-platform problems.
Drive delivery across both organisations, resolving technical disagreements, dependencies, and risks.
Communicate architecture, progress, trade-offs, risks, and results clearly to audiences ranging from implementation teams to senior executives and C-suite leaders.
Turn learning from the Mayo Clinic deployment into reusable platform investments, integration patterns, tooling, and product-roadmap recommendations.