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.

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Autonomous Systems

Teaches machines to move

Autonomous Systems

Summary

Known as: Perception Engineer, Robot Learning Engineer, Robotics Engineer, Autonomous Vehicles Engineer, Robotics ML Engineer, Simulation Engineer, Research Scientist (Robotics), Research Scientist (World Models), Research Scientist (Manipulation), Research Scientist (Locomotion), RL Research Engineer, Research Engineer (Embodied Systems), Simulation Engineer (Scenarios), Test Engineer (Autonomy)

Trains and deploys perception, planning, and control models on physical hardware — robots and autonomous vehicles that must decide and act safely in real time. Spans research through production: develops new learning methods (sim-to-real transfer, behavior cloning, reinforcement learning) and ships them on real hardware. At most companies, the research and engineering are deeply intertwined.

Specializations

Perception — Sensor fusion (camera, LiDAR, radar, tactile), object detection and tracking, scene understanding, and semantic mapping. Turns raw sensor streams into a reliable model of what surrounds the system and how it's changing.

Learning & Control — Learning control policies from rewards, demonstrations (behavior cloning), or simulation. Grasping, walking, driving, manipulation. Combines learned policies with control systems (MPC, force control) that keep behavior stable and safe. Includes sim-to-real transfer and domain randomization to bridge the gap between simulated training and real-world deployment.

World Models & Planning — Predictive models of how the environment will evolve — what objects and agents will do next — and using them for planning and decision-making under uncertainty. Uses learned world models rather than hand-engineered planners.

Embodied Foundation Models — Large vision/language models for physical systems: instruction understanding, scene reasoning, and action planning while meeting real-time and on-device deployment constraints. The convergence of foundation model capabilities with embodied systems requirements.

Physical Safety — Collision avoidance, emergency stop systems, failure modes analysis, human-robot interaction safety, and functional safety standards (ISO 26262, IEC 61508). For autonomous vehicles and robots, combines model alignment with domain-specific safety engineering. Includes tamper detection, secure boot, and safety certification processes.

World models research splits between embodied world models (planning and control for physical systems) and general predictive modeling (V-JEPA, Sora-style video generation).

Where the Work Lives

[1]Substrate

[2]Compute

Secondary

Training perception and world models demands heavy compute; on-device inference has strict constraints.

[3]Intelligence

Primary

Develops perception, learning, and world models that give physical systems intelligent behavior.

[4]Systems

Primary

Builds the planning, control, and safety systems that make autonomous operation reliable.

[5]Distribution

Candidate Archetypes

Philip Wagener

Waymo

Perception & fusion

Turns camera, LiDAR, radar, and tactile streams into stable object tracks and scene state under edge-case pressure.

Kenneth Cari

Tesla

Policy & control

Builds learned policies (BC/RL) and integrates them with control systems (MPC, force control) to stay stable on hardware.

Nate Walker

Figure

Planning & safety

Owns predictive planning under uncertainty and the functional safety case that proves the system is safe to deploy.

Company Scale

Early-Stage

Occasional

Growth

Common

Enterprise

Primary

Bimodal: founding hire at robotics and AV startups; absent at SaaS AI companies. Growth+ specializes across perception, planning, and control.

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