SA
Founding Engineer
Accepting applicationsStealth AI Infrastructure Startup · San Francisco Bay Area
Full-Time Mid_senior AIC++Pythonaiate
Posted
4 May
Category
Test
Experience
Mid_senior
Country
United States
Founding / Early Engineer - Real-Time AI Systems (Healthcare)
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.)
Kafka
Kubernetes
Python
TensorFlow
Rust and/or Golang
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
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
Candidates coming from NVIDIA OR FAANG
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
Compensation & Benefits
$225,000 + base salary, DOE
Early-stage equity
Full health benefits
401(k)
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.
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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.)
Kafka
Kubernetes
Python
TensorFlow
Rust and/or Golang
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
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
Candidates coming from NVIDIA OR FAANG
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
Compensation & Benefits
$225,000 + base salary, DOE
Early-stage equity
Full health benefits
401(k)
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.
Show more Show less