Drawing on Metix AI's 860M+ global talent pool, we profile in full 13 embodied-AI and humanoid-robot players (pure-play embodied startups + in-house robotics groups at the tech giants): in a sector where valuations have exploded but the talent pool stays razor-thin, where do the people actually come from, how many are there, and who is reachable.
The figures below follow Metix AI database methodology (data through H1 2026); the population = talent currently employed at 13 embodied-AI players, based mainly in the US (incl. 1X in Norway).
Sector valuations have reached Figure $39B, Skild $14B, and Physical Intelligence $5.6B (with $11B reportedly under discussion), yet visible technical talent totals only 1,044 people, spread across 13 players. The most sought-after pure-AI humanoid / foundation-model teams are especially lean (Physical Intelligence, Skild, and The Bot Company each show only a few dozen visible technical staff). The gap between deep capital and thin talent is the underlying constraint on every hiring move in this market.
26.4% of the technical pool is hardware/mechatronics (actuators, mechanical, electronics, embedded) — the second-largest function after software; add motion control and the like, and the engineers who build the "body" account for nearly 30%. This is the most fundamental difference between embodied AI and both autonomous driving and AI labs, where hardware roles are close to zero. The people who build the body and the people who build the brain come from entirely different supply pools, so recruiting strategy has to run on separate tracks.
Overall, 8.2% have an AV background, but it is highly concentrated in the software / foundation-model teams: 46.0% of technical staff at Bedrock Robotics, 37.9% at The Bot Company, and 30.0% at Tesla Optimus come from autonomous driving. The top three feeders are Cruise, Zoox, and Waymo — all major sources of AV talent released in recent years. At hardware-centric companies (Boston Dynamics, Apptronik), the AV share is in the single digits.
26.4% of the technical pool is hardware/mechatronics; add motion control, and the engineers who build the "body" account for nearly 30%. People explicitly working on foundation models/VLA, robot learning, or manipulation number from a handful to a few dozen each. The software / foundation-model layer overlaps heavily with AV / AI labs, while the hardware layer has independent supply — recruiting has to run on separate tracks.
The following draws on public sources from 2025-2026 (full list in the research file), keeping only facts that bear on talent judgments. Valuations follow reported / confirmed figures, with unverified items flagged.
Robotics funding in 2025 ran on the order of $40B for the year. Confirmed figures: Figure $39B (2025-09), Skild $14B (2026-01 — a 3x jump in seven months), Apptronik $5B (2026-02), Physical Intelligence $5.6B (2025-11; $11B reportedly under discussion). NVIDIA has invested in nearly every leading embodied-AI company while building its own GR00T foundation model and Isaac simulation platform — a dual play of "selling shovels + building models."
Figure 02 has logged 1,250+ cumulative hours at BMW's South Carolina plant; Agility's Digit and Apptronik's Apollo are in testing at GXO / warehousing; Boston Dynamics' electric Atlas production fleet for 2026 has been pre-ordered by Hyundai. By contrast, Tesla Optimus still had no external customers as of early 2026 and remains in the R&D and data-collection stage. These deployment differences directly shape each company's demand structure across functions (production engineering vs. research).
The mainstream is vision-language-action (VLA) models + imitation learning + teleoperated data collection, with world models and "reinforcement learning from real-world experience" on the rise (Physical Intelligence's π0.6 introduces real-environment RL). The foundation-model path (PI / Skild / NVIDIA GR00T / Gemini Robotics) runs in parallel with the build-your-own-hardware path (Figure / Tesla / Apptronik / 1X).
Four feeders: autonomous driving (perception / planning / data flywheel / sim2real skills transfer directly; the most fully-formed), academia (robotics labs at CMU / Stanford / Berkeley / UW), tech-giant ML/AI, and traditional robotics (the half that builds the hardware). Embodied AI is one of the few sectors that needs large numbers of both "software brain" and "hardware body" people at the same time.
Population = 1,044 technical staff (research / foundation models / manipulation / motion control / perception / simulation / hardware / robotics software).
Read: Boston Dynamics and Apptronik have the largest visible technical pools (big teams with many hardware roles), while the highest-valued pure-AI humanoid / foundation-model companies (Physical Intelligence, Skild, The Bot Company, Dexterity) each show only a few dozen visible technical staff. The "valuation-vs-headcount inversion" is plain to see in this chart, and it explains why every senior candidate in this sector is fought over again and again.
Read: software/AI (general) is the largest, but hardware/mechatronics is the second-largest function at 26.4%; add motion control and mechatronic systems, and the engineers who build the body account for nearly 30% (against the near-zero hardware roles at the latter two — AV and AI labs — this is embodied AI's structural fingerprint). The people explicitly working on foundation models/VLA, robot learning/RL, and manipulation are very few (from a handful to a few dozen each), the scarcest and hardest-to-find frontier profiles in the entire market.
Read: this chart breaks the "assets" down to the function-cell level. The dark hardware/mechatronics cells cluster at build-your-own-hardware companies like Boston Dynamics, Apptronik, Agility, and 1X; the research scientists and foundation-model people cluster at NVIDIA GEAR, DeepMind Robotics, and Physical Intelligence. In other words, to hire the "body-builders" you go to the hardware-platform companies, and to hire the "brain-builders" you go to the tech-giant research groups and foundation-model unicorns — the two pools barely overlap.
Read: top-right, Boston Dynamics and NVIDIA GEAR (median 22 months, more than a quarter past the 4-year cliff) are "mature teams," where more talent has entered the window of actively looking; bottom-left, The Bot Company, Bedrock, and 1X (median 9-10 months, almost no one past the cliff) are still in the team-building honeymoon, with high retention. Founding date and team maturity directly determine how easy it is to talk to a company's people right now.
Embodied-AI talent comes mainly from four places: autonomous driving, academia, tech-giant ML, and traditional robotics. Of these, autonomous driving is the most fully-formed and the best skills match, and it is where this sector connects seamlessly with the AV talent market.
Read: the sources are highly dispersed, confirming this is a new sector "pulling people in from all directions." Autonomous driving is the most fully-formed and best-matched of them; what it supplies is not the largest in number but the highest-quality, team-level migrating "brain" talent (see 4.2). Direct supply from academia and tech-giant ML follow closely, while traditional robotics mainly supplies the hardware layer.
Read: this is the most information-rich chart in the whole report. Bedrock Robotics (46.0%), The Bot Company (37.9%), and Tesla Optimus (30.0%) are almost "built on autonomous-driving DNA": Bedrock is the former Waymo trucking team, The Bot Company is the former Cruise crew, and Optimus directly reuses Tesla FSD's AI stack. By contrast, Boston Dynamics, Apptronik, and DeepMind Robotics have single-digit AV shares — they are either hardware-heritage or tech-giant-research lineage.Conclusion: autonomous driving feeds embodied AI's "software brain," not its "hardware body."
Read: Cruise, Zoox, and Waymo are the top three feeders — precisely the AV companies that have been "clearing out / contracting" in recent years (Cruise shut down, Zoox slow to commercialize). A sizable share of that released or change-seeking AV talent has embodied AI as its next stop: autonomous driving's "outflow" is embodied AI's "inflow."
Read: this is the "wiring diagram" of talent flowing from autonomous driving into embodied AI. The thickest line is Waymo → Bedrock Robotics (the former Waymo trucking team spun out as a whole), followed by Cruise → The Bot Company (the former Cruise crew). Cruise alone feeds The Bot Company, Agility, Bedrock, and NVIDIA GEAR at once — a true "cradle of embodied-AI talent." These named flows turn the abstract "8.2% with an AV background" into real, traceable team migrations.
Read: among today's technical staff, 2025 hires are 3.4x those of 2023 — the bulk of this sector's teams were built almost entirely in the last two years. The autonomous-driving source (purple) thickens markedly in this 2024-2025 wave, matching the timeline of Cruise's shutdown and Zoox/Waymo's contraction: embodied AI's hiring spree is keeping step with the AV clear-out.
From 1,930 profiles, three groups totaling 20 people were hand-picked by seniority, scarcity of specialty, and strength of track record. Profiles come from the Metix AI database; those tagged "publicly verified" have had their current role confirmed against 2025-2026 public sources. The "AV background" tag marks those with an autonomous-driving track record. Names are masked by default in the public version.
Pay tiers in embodied AI are extreme. Figures follow reported / levels.fyi / H1B sources retrieved 2026-06; not official company data.
| Player | Engineer reference TC | Top research roles | Equity profile | Notes |
|---|---|---|---|---|
| NVIDIA GEAR | Robotics roles ~$333K | Foundation-model research $400K+ | NVIDIA public stock | Brain-building layer, benchmarked to AI labs |
| Physical Intelligence | Researchers $300-475K+ | Heavy early-stage equity leverage | Unicorn options ($5.6B) | Lean team, scarce per head |
| Figure AI | H1B median ~$230K | Helix research higher | $39B-valuation options + $100M employee buyback | Retaining people via buyback |
| Tesla Optimus | Reuses the Tesla system | n/a | Tesla stock | Low base + stock leverage |
| Apptronik / Agility | ~$122-135K | n/a | Private-company options | Hardware roles + location, markedly below the brain-building layer |
| Teleoperation / data collection | $25-35/hour | n/a | None | The other pole of the sector's pay |
The total package for a top robotics foundation-model researcher ($300-475K+ in equity) differs from a bottom-tier teleoperated data collector ($25-35/hour) by more than an order of magnitude; engineering roles at hardware-centric companies (around $122-135K) are also clearly below the brain-building layer. Within a single "embodied-AI talent" report there are three almost unrelated pay markets.
The brain-building layer (foundation models / robot learning / perception) competes with autonomous driving and AI labs for the same people, pushing pay close to AI-lab levels; the hardware / mechatronics layer has relatively independent supply (from traditional robotics, automakers, and aerospace), with far less bidding pressure. Recruiting budgets should be priced separately for these two markets.
The same dataset means different things to headhunters, robotics-company HR, and VCs.
① This is a "thin but expensive" market where precision beats broad outreach: the brain-building layer (foundation models / robot learning / manipulation) totals only a few dozen to just over a hundred people, worth cultivating one by one; ② autonomous driving is the best-matched adjacent pool — the perception / planning / sim2real talent from "clearing-out / contracting" AV companies (the Cruise / Zoox / Motional camp) is a ready-made source for embodied brain-building roles; ③ hardware / mechatronics roles must be sourced from traditional robotics, automakers, and aerospace, on a completely separate track from brain-building roles; ④ Brain-building talent is most reachable along alumni and university lines (SJTU / Tsinghua / Zhejiang + Stanford / Berkeley / UCSD / CMU).
① Use this report to locate where you sit on the scale and function-structure map; ② brain-building roles bid head-to-head against AV / AI labs, and your differentiation rests on the tangible payoff of "algorithms directly driving a real body" plus early-stage equity; ③ hardware roles have independent supply and gentler competition, a more controllable surface to expand on; ④ mind the sector's high churn ("other / startups" is the largest source) — retention needs a clear technical roadmap and milestones.
① A team's autonomous-driving DNA is a quantifiable due-diligence signal: the high AV shares at Bedrock / The Bot Company / Optimus correspond to teams that are "fully formed, with data-flywheel experience"; ② the inversion of valuation against talent depth flags risk — a few-dozen-person team supporting a multibillion-dollar valuation takes a heavy hit if a key person leaves; ③ academic founding teams (the CMU / Stanford / Berkeley camp) and industry teams (the AV camp) are two different bets, the former strong on research, the latter strong on engineering deployment.
The search, profiling, and migration analysis in this report were all done by Metix AI. We can build a custom talent map for any company on the same methodology: a full list, a source-migration network, email unlocking, and multi-channel outreach — billed on a "pay only for qualified interviews" basis. No interview, no charge.
860M+ global talent profiles1,044-person embodied technical poolAV→embodied migration trackingPay only for qualified interviewsPure-play embodied startups: Physical Intelligence, Figure AI, Skild AI, 1X Technologies, Apptronik, Agility Robotics, Dexterity, The Bot Company, Bedrock Robotics, Boston Dynamics. In-house robotics groups at the tech giants: Tesla Optimus, Google DeepMind Robotics, NVIDIA GEAR. The latter three are subsets identified by robotics-function keywords, not whole companies. Geography is mainly the US, including 1X in Norway and others.
Technical talent pool = foundation models/VLA, robot learning/RL, manipulation, motion control, perception, simulation, teleop data, hardware/mechatronics, robotics software, research scientists, software/AI (general), founders/executives.
"Has an autonomous-driving background" = past employers include Waymo/Cruise/Zoox/Nuro/Aurora/Argo/Motional/Pony.ai/WeRide/TuSimple/Kodiak/Gatik/Torc/Wayve and others. Tesla, given that it spans both automotive and robotics, is excluded from the strict AV determination to avoid overcounting.
Data is current through H1 2026; the source data for AV→embodied migration is based on the same talent pool. Key figures have been verified one by one against public information.
| Player | Current profiles | Technical pool | AV-background share | PhD rate |
|---|---|---|---|---|
| Boston Dynamics | 455 | 297 | 2.0% | 12.5% |
| Apptronik | 316 | 146 | 2.7% | 15.1% |
| Agility Robotics | 274 | 131 | 6.1% | 13.7% |
| 1X Technologies | 227 | 101 | 4.0% | 7.9% |
| Figure AI | 200 | 94 | 8.5% | 22.3% |
| NVIDIA GEAR | 90 | 73 | 12.3% | 21.9% |
| DeepMind Robotics | 86 | 59 | 1.7% | 35.6% |
| Bedrock Robotics | 82 | 50 | 46.0% | 14.0% |
| Tesla Optimus | 47 | 30 | 30.0% | 26.7% |
| The Bot Company | 81 | 29 | 37.9% | 3.4% |
| Skild AI | 27 | 15 | 6.7% | 20.0% |
| Physical Intelligence | 28 | 11 | 18.2% | 36.4% |
| Dexterity | 17 | 8 | 0.0% | 0.0% |
① This report aggregates public professional profiles, so larger teams and those that maintain LinkedIn thoroughly have higher coverage; small pure-research teams (Physical Intelligence / Skild / Dexterity) have smaller samples, and their absolute counts should be read as lower bounds. ② The tech-giant robotics groups are function-identified subsets and diverge from the companies' true robotics org charts. ③ Function and level are inferred from title/headline keywords. ④ Comparisons across players rely mainly on shares and structure, with absolute counts secondary.