Artificial Intelligence Researcher

Are you an established medical imaging / deep learning engineer who’s tired of shipping models that never leave a research folder?

Maybe you’re sitting inside a massive tech or Med Tech org where you’re one contributor among hundreds… and you’re ready to actually own the work, move fast, and see your algorithms integrated into real systems.

This is your chance to join a rapidly growing robotics + AI company building next-generation imaging and guidance workflows—where your work won’t be theoretical.

It will be deployed.

It will be tested in real environments.

And it will matter.

Working in a highly technical, onsite R&D environment, you’ll help drive deep learning and medical imaging development that directly supports robotic planning, guidance, and execution workflows.

This is not a "model training only" role.

It’s hands‑on engineering work that combines:

• deep learning
• medical imaging
• real‑time performance constraints
• and production‑level C++ development

You’ll be surrounded by robotics engineers, software engineers, and applied R&D talent who are all building toward the same goal: a working system, not a slide deck.

This is a small‑team environment where you can actually make an organizational dent.

You’ll have immediate ownership.

Immediate impact.

If you’ve been craving the kind of role where you can build end‑to‑end pipelines, influence the direction of the tech, and see your work integrated quickly—this is it.

• Prefers building over maintaining
• Wants real accountability and high ownership
• Enjoys performance constraints and production realities
• Can collaborate quickly and iterate without layers of bureaucracy
• Thrives in an onsite, high‑feedback engineering culture
• Wants to move fast and be close to the robotics team

If you don’t have these, this likely won’t be the right fit:

• Real experience in medical imaging, specifically one or more of:
• registration
• tracking / vision‑based imaging workflows
• Strong C++ (this is the core production language)
• Deep learning experience (Python is common for development, but C++ is key for deployment)
• A practical engineering mindset — you’ve shipped real systems, not just experiments

• Exposure to image‑guided clinical workflows (interventional, procedural, surgical, etc.)
• Experience optimizing inference and performance (Tensor
  
  RT or similar stacks)
• Robotics‑adjacent software experience (latency awareness, systems integration mindset)