Founding Engineer

Founding / Early Engineer - Real-Time AI Systems (Healthcare)About the Role

We’re building something that doesn’t exist today: real-time AI that runs inside a doctor’s office.

Today, AI in healthcare mostly lives in the cloud. It helps with notes and paperwork—but it’s not present when decisions are made. That’s because clinics don’t have the power, cooling, or infrastructure needed to run high-performance systems.

We’re changing that.

We’re building GPU-powered systems that run locally, inside clinical environments, enabling AI to operate in real time during patient care. This is a move from prototype to production, and early engineers will define how the system works end-to-end.

What You’ll Do

You’ll take ownership of systems from start to finish, working across:

* Infrastructure (running GPU compute in constrained environments)

* Backend systems & integrations (making messy clinical software actually work)

This is not a narrow role—you’ll work across hardware, infrastructure, and application layers to build systems that are reliable in the real world.

Must-Have Technical Expertise

We care about deep understanding and systems thinking, not just tool familiarity. You should be able to explain how these technologies work together in real, production systems.

NVIDIA / GPU Stack (Highest Priority)

This role is CUDA-centric. Our entire system runs on NVIDIA GPUs.

Required experience:

* CUDA (non-negotiable)

* TensorRT

* PyTorch + C++

* Low-level performance optimization (memory, compute, thermals, etc.) -- This is critical for building high-performance AI systems that run locally (not in the cloud).

Infrastructure & Distributed Systems

* Kafka (event-driven architecture)

* Kubernetes (container orchestration)

You should understand how to build scalable, reliable systems under real load.

AI / ML Integration

* Python

* TensorFlow and/or PyTorch

Experience deploying models into real systems—not just training them.

Systems Programming

* Rust and/or Golang

Used to build fast, reliable systems that operate close to the hardware.

What You’ll Be Building

* Private, local AI systems (runs inside the clinic, not the cloud)

* Edge deployments (works in constrained environments with limited power/cooling)

* Decentralized AI infrastructure (data stays local and owned)

* Systems that remain stable under real-world conditions

This is about pushing the boundaries of where AI can operate.

What Makes This Role Different

* You own outcomes, not tickets

* You’ll work with incomplete specs and real constraints (power, heat, noise)

* There is no clean separation between hardware, infra, and software

* If the system fails, care is impacted—reliability matters

What We’re Looking For

* Experience building and owning production systems under real load

* Strong systems thinking (understanding failure modes, performance, tradeoffs)

* Ability to operate in ambiguity without a playbook

* Curiosity beyond your core domain

Helpful backgrounds:

* Infrastructure / platform engineering

* Distributed systems

* GPU / high-performance computing (HPC)

* Real-time systems (games, robotics, autonomous systems)

* Clinical or regulated environments

Compensation & Benefits

* $150,000 – $210,000 base salary, DOE

* Meaningful early-stage equity

* Full benefits (healthcare + 401k)

Who This Is NOT For

* Engineers who prefer clearly defined tasks and boundaries

* Pure frontend or CRUD-focused backend developers

* Data scientists without production deployment experience

Why Join

This is a rare chance to:

* Build net-new infrastructure for real-time AI

* Work at the intersection of GPU systems, AI, and healthcare

* Have true ownership from day one on a small, high-impact team

Additional Job Application Terms:

This job is part of LinkedIn’s Full-Service Hiring beta program. Eligibility is limited to candidates located in and performing services in the United States, excluding those based in Alaska, Hawaii, Nevada, South Carolina, or West Virginia.

We’re committed to making our hiring process as smooth and timely as possible, and we understand that waiting to hear back can add to the anticipation. If you’re a potential fit, our team will reach out within two weeks to progress you to the next stage. If you don’t hear from us in that time, we encourage you to explore other opportunities with our team in the future, and we wish you the very best in your job search.