Private Draft

The 29 personas behind AI

We’ve organized every stage and persona in the AI supply chain, informed by real recruiting at frontier companies. Click any row to see matching profiles from our talent graph.

Shaped by Industry Experts
Kumar Chellapilla
Kumar ChellapillaVPE
Jennifer Anderson
Jennifer AndersonVPE / Stanford PhD
Thuan Pham
Thuan PhamCTO
Akash Garg
Akash GargCTO
Linghao Zhang
Linghao ZhangResearch Engineer
Wayne Chang
Wayne ChangEarly FB Engineer
Indrajit Khare
Indrajit KhareEM & Head of Product
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Robotics Platforms

Ships robots that work reliably
Robotics Platforms

Summary

Known as: Robotics Systems Engineer, Systems Engineer, Controls Engineer, Simulation Engineer, Embedded Systems Engineer, Field Robotics Engineer, Fleet Operations Engineer, Field Service Engineer (Robotics), Autonomy Operations Engineer

Infrastructure and systems engineering that makes robots and autonomous vehicles work reliably in the real world — from deployment pipelines and fleet operations through on-device runtime, controls, and simulation.

Specializations

Infrastructure — How you ship and operate fleets: deployment and updates, diagnostics, monitoring/observability, and data collection pipelines. The platform layer for robots and autonomous vehicles.
Runtime — The on-device "nervous system": getting sensor data through the stack with low latency, running real-time inference, and doing OS/performance work so models run consistently on hardware.
Controls — The algorithms that turn intent into stable motion: classical control, model predictive control (MPC), force control, and the real-time systems that keep behavior smooth and safe.
Simulation — Virtual testbeds: physics simulation, digital twins, and large-scale sim environments used to test edge cases, generate synthetic data, and help transfer policies from sim to real.
Fleet Operations — Operates deployed robots and autonomous vehicles in production. Fleet monitoring, field diagnostics, remote intervention, over-the-air (OTA) updates, uptime optimization, and incident response. The site reliability engineering of physical AI.

Where the Work Lives

[1]Substrate
[2]Compute
Secondary

On-device runtime, real-time inference, and the performance engineering that makes robots responsive.

[3]Intelligence
Secondary

Simulation environments and data pipelines that feed the learning and evaluation loop.

[4]Systems
Primary

Deployment infrastructure, controls, fleet operations, and the platform layer for physical AI.

[5]Distribution

Candidate Archetypes

Sriram Gupta
Sriram Gupta
Boston Dynamics
Fleet platform & OTA

Owns deploy, update, and diagnose loops — remote intervention and uptime optimization for robots in the field.

Trey Pickard
Trey Pickard
NVIDIA
Runtime & controls

Makes the on-device nervous system deterministic, low-latency, and safe enough for physical motion.

Ludvik Lenn
Ludvik Lenn
Tesla
Simulation & digital twin

Builds virtual testbeds for edge cases, synthetic data generation, and sim-to-real iteration.

Company Scale

Early-Stage
Common
Growth
Primary
Enterprise
Primary

Any robotics org. Generalists early; growth+ specializes into controls, sim, infrastructure.

Featured Roles

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