Who Figure Hires
Three years after filing incorporation papers in Delaware, Figure AI has a humanoid prototype walking a BMW body shop in Spartanburg every production day. Robots move sheet metal between stations. A second logistics customer is preparing its own deployment. A manufacturing line rated for 12,000 units a year is online. The founder's framing is blunt: "We don't have a GPA problem. We have a data problem and a robot problem." That statement doubles as a hiring filter.
The team structure reflects the vertical integration Figure insists on. The company "does all of the AI work ourselves" because "we do AI work on humanoid robots better than anybody on the planet," said in a Bloomberg Live interview. That claim maps to six Helix AI Engineer postings — Perception, Localization and Mapping, Backend, Reinforcement Learning, Pretraining, and iOS — all based in San Jose, each carrying a $200,000–$400,000 band, Zero G Talent's board data shows (detailed in the compensation section below). Helix is the company's vision-language-action model, built on an open-source Dex backbone.
Hardware and manufacturing run in parallel. The Figure 3 robot comes in at roughly 90 percent cost reduction over Figure 2, a number that only makes sense if mechanical, electrical, and supply-chain teams iterate in lockstep with software. The Spartanburg deployment produces a daily metrics readout the founder reviews personally. "I have a readout every day. At the end of the day, the mom of the team that's in Spartanburg and we get to figure out how to make the process better," said in a Bloomberg Live interview. That loop — robots working, breaking, generating data, improving — is the actual product.
Teleoperation plays a specific, bounded role: data collection for training, not remote piloting in production. "We use teleoperation for our data collection efforts and other types of testing, but we do none of this in real autonomous activities," said in a Bloomberg Live interview. The distinction matters for hiring. Candidates who have only operated robots in lab settings, or who treat teleop as a crutch for autonomy gaps, don't match the profile. Figure wants people who have stared at the sim-to-real gap and found ways to close it with fleet data.
That language — a specific, observed behavior from a live facility — is the granularity Figure expects its engineers to care about.
Across every function, the through-line is speed. The founder avoids conferences ("everything else is basically distraction"), ignores competitors ("we don't spend too much time looking at competitors"), and measures progress in shipped units that earn revenue. The hiring signal is consistent: show a working prototype you built, explain the hardest integration problem you solved, demonstrate you can operate without a safety net of process. The rest is noise.
Pay and Equity
Figure AI's compensation structure reflects a company betting heavily on elite robotics and AI talent. Zero G Talent's board data for Figure AI shows 89 salaried roles spanning $62,000 to $400,000 annually, with a median of $220,000. That spread captures everything from junior test technicians to the Helix AI engineers driving the company's core perception, planning, and learning stacks.
The clearest signal comes from six recent Helix AI Engineer postings, all San Jose, all listing identical ranges of $200,000–$400,000. The roles span iOS, Localization and Mapping, Perception, Backend, Reinforcement Learning, and Pretraining. That uniformity suggests Figure prices its senior AI/robotics software talent on a single high-end band rather than fragmenting by specialty. A Helix AI Engineer working on Pretraining commands the same ceiling as one building the iOS deployment layer; the company values end-to-end system contribution over narrow domain premiums.
| Role | Location | Posted Salary Range |
|---|---|---|
| Helix AI Engineer, iOS | San Jose, CA | $200,000–$400,000 |
| Helix AI Engineer, Localization and Mapping | San Jose, CA | $200,000–$400,000 |
| Helix AI Engineer, Perception | San Jose, CA | $200,000–$400,000 |
| Helix AI Engineer, Backend | San Jose, CA | $200,000–$400,000 |
| Helix AI Engineer, Reinforcement Learning | San Jose, CA | $200,000–$400,000 |
| Helix AI Engineer, Pretraining | San Jose, CA | $200,000–$400,000 |
Source: Zero G Talent board postings for Figure AI (first-party data).
Equity details do not appear in the board postings. Candidates should ask recruiters for the current 409A valuation, the percentage of fully diluted shares the grant represents, and whether the company offers early-exercise options (standard diligence for any pre-IPO robotics venture).
The compensation picture aligns with the hiring theme: Figure pays for engineers who deliver working prototypes, not paper architectures. The $200k–$400k band for Helix roles is the market's way of saying "show us the robot walking."
Inside the Interview Loop
Figure has not published its interview process. Candidates should ask their recruiter for the current stage count and any practical assessments; transparency varies by hiring manager.
What the roles reveal about screening: each Helix posting maps to a distinct subsystem of a humanoid: perception (vision, sensor fusion), localization and mapping (SLAM, state estimation), reinforcement learning and pretraining (policy learning, sim-to-real), backend (data pipelines, training infrastructure), and iOS (on-device inference, robot-phone integration). A recruiter screening for these roles looks for evidence you have owned a subsystem from simulation through hardware bring-up. That means:
- A portfolio of shipped robotics systems: GitHub repos with hardware logs, videos of real-robot runs, or links to deployed products.
- Explicit sim-to-real narrative: describe the gap you closed, the metrics you tracked, and the hardware constraints you respected.
- End-to-end ownership language: "I owned perception from sensor selection through on-robot inference at 30 Hz" beats "I improved detection mAP by 3 points."
- San Jose readiness: all current Helix roles are on-site; remote flexibility is not advertised.
- No hardware loop closure: if your experience stops at simulation or offline evaluation, you will not clear the on-site.
- Single-thread specialization: e.g., only model training, only firmware, only mechanical design.
- Visa sponsorship uncertainty for senior roles: the $200k–$400k band suggests senior IC or staff level; immigration timelines can derail offers.
- Misaligned pace signals: candidates who describe 6-month research cycles without interim hardware milestones rarely match Figure's weekly-iteration cadence.
The common thread: system integration. Figure's public demos (walking, manipulation, whole-body coordination) require tight coupling between perception, planning, and control. Candidates who only know one layer (e.g., model training without deployment) tend to stall.
The through-line is clear: Figure hires engineers who have integrated perception, planning, and control on a physical robot and can prove it. The interview is designed to verify that you've done the work, not just studied it.
San Jose Campus
Figure's hiring footprint centers on San Jose, California. Every Helix AI Engineer role posted to our board in recent months lists San Jose as the work location. That concentration signals a single primary campus rather than a distributed multi-site model, at least for the software-heavy roles the board captures.
The board data shows no postings for facilities technicians, lab managers, or safety officers, roles that would appear once the campus reaches steady-state scale. Their absence suggests Figure is still in a growth phase where the core engineering team absorbs facility operations, or that those hires happen through channels our board does not yet index.
In Spartanburg, the robots still smooth the plastic so the barcode shows. That image, a humanoid adjusting its grip on a real production line, generating the data that closes the loop, is the only culture document Figure has published. The rest is noise.
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