Robotics and AI organizations don't fit a generic role chart. This is how we think about the talent — grouped by function, calibrated by stage, and screened against the skills that actually matter.
The roles that own the autonomy stack — perception, state estimation, planning, and control. Where most of our deepest searches happen.
2D/3D object detection, segmentation, tracking. Camera, lidar, radar, fusion. Often a CV or ML background with deployment chops.
Visual-inertial odometry, factor graphs, multi-sensor fusion. The folks who can debug a drifting map at 2am.
Sampling-based and optimization-based planners. From wheeled platforms to manipulation to humanoid locomotion.
MPC, LQR, classical and learned control. Deep familiarity with the dynamics of the platform — wheeled, legged, aerial, or arm.
ROS / ROS 2, real-time systems, system integration, behavior trees. The generalist who keeps the autonomy stack glued.
Isaac Sim, Gazebo, custom simulators. Synthetic data, domain randomization, sim-to-real evaluation harnesses.
The applied ML and platform engineers who get models off the lab GPU and into the robot — or the cloud workload that powers it.
Foundation models, world models, behavior cloning, imitation, reinforcement learning. Publication track or equivalent industry depth.
Train, fine-tune, evaluate, ship. Strong on data, eval, ablation, and the pragmatic engineering between research and production.
Distributed training infra, GPU orchestration, model registries, eval pipelines. The folks who make the ML team's lives possible.
Continuous eval, regression detection, dataset versioning, model deployment, drift monitoring. Rigor over heroics.
Petabyte-scale ingestion, log processing, training data curation, query layers. Often the unsung hero of an ML org.
Pre-training, post-training, RLHF, distillation, evals. Embodied-AI variants for robotics applications.
Robotics is an integration sport. We recruit the engineers who design, build, and integrate the actual machines — and the firmware that makes them move.
RTOS, C/C++, low-latency communication, sensor drivers, motor control. Bare-metal to Linux userspace.
Cross-disciplinary engineers who think across mechanical, electrical, and software. Often the technical backbone of an early robotics team.
Mechanism design, structural analysis, DFM. From prototype CAD to mass production tooling and supplier qualification.
Schematic, layout, power electronics, signal integrity. Robotics-specific: motor drivers, battery systems, sensor frontends.
Hardware-in-the-loop, environmental, durability, regulatory. Builds the test infrastructure that catches failures before customers do.
NPI, supply chain, contract manufacturing, quality. The leaders who turn a working prototype into a shipped product.
A great robotics or AI team is more than its engineers. We recruit the product, operations, and leadership talent that lets the engineering bet pay off.
PMs who can spec a behavior, write a model card, and translate a customer need into a roadmap. Rare and valuable.
Builds and runs labeling pipelines and QA loops, often with offshore teams. Treats data ops as engineering, not back-office.
The person who lives at the customer site and makes the robot actually work in the wild. Half engineer, half customer success.
Senior leaders who've built and scaled robotics or AI orgs through the next stage. Hire calibration, panel design, technical strategy.
The first engineering leader at a new robotics or AI startup. We've placed several. We know what closes and what doesn't.
The TA leader who'll build the function we'd otherwise embed. Often a hire we facilitate after a year of running RPO.
If it touches robotics, autonomy, or applied AI, we recruit for it. Send us the JD and we'll tell you what's realistic on comp, geography, and timing.