Building the
category-defining layer.
Xolver is building the layer between learned perception and physical action. That means robotics foundation models, deterministic control boundaries, and the infrastructure required to make machine behavior useful in the real world.
We are looking for people who want to work on world models, enforcement, edge execution, and the messy realities of deploying intelligence into machines.
- Physical Intelligence Infrastructure
- Deterministic Actuation
- Human-Machine Synergy
Noida is our crucible.
This work happens close to hardware. Our Noida lab is where model behavior, controls, sensors, and real machine constraints meet, which is why many roles require proximity to the systems we are building.
Architecting the future.
These are roles for people who want to work on technical systems that touch hardware, operations, and deployment reality. Explore the missions available today.
Head of Growth
Define and own the global go-to-market for robotics foundation models and edge devices. No template exists so you must build it.
Role Brief
Physical AI Engineer (Internship)
Build, train, and fine-tune neural networks for tasks such as spatial awareness, object manipulation, and trajectory prediction.
Role Brief
Founding ML & Foundation Model Engineer
Design and train large-scale foundation models and specialized policies (e.g., Diffusion Policy, ACT) that enable robots to perform complex tasks with high success rates.
Role Brief
Founding Embedded & Safety Systems Engineer
Design and implement the "enforcement layer" that acts as a hard-coded safety governor, mathematically ensuring the robot stays within safe operating envelopes.
Role Brief
Founding Robotics & Autonomy Engineer
Lead the physical assembly and calibration of robotic platforms (e.g., ALOHA or Koch rigs). Ensure actuators, encoders, and sensors are synchronized.
Role Brief
Strong fit, no exact role.
If your background cuts across physical intelligence, world models, deterministic robotics, controls, or industrial deployment and you do not see a clean fit yet, reach out directly.
"People who want to work where models meet hardware, and where technical decisions have physical consequences."