Figure AI
Humanoid robotics company combining foundation models with real-world embodiment.
Typical Backgrounds
Types of experience professionals commonly develop
Roles Companies Often Recruit
Common roles when hiring Figure AI-caliber talent
- AI Engineers
- Research Engineers
- Applied Scientists
- Infrastructure Engineers
- Founding Engineers
- Engineering Leaders
Related Resources
Salary guides, hiring guides, market reports and case studies
Talent market position
Figure AI builds humanoid robots and sits at the intersection of robotics hardware, embodied AI, and controls engineering, a talent pool distinct from most companies on this list because it requires deep mechanical and electrical engineering alongside modern AI/ML skills. Search interest concentrates on the embodied AI/robot learning team (applying foundation-model techniques to robot control) and on core hardware and controls engineering, both genuinely scarce skill sets given how few companies build humanoid robots at this stage of maturity.
The company's high-profile partnerships and funding rounds have kept it visible in both the robotics and AI talent markets simultaneously, drawing interest from engineers who want to work on physical AI rather than purely digital/software AI products. This dual appeal is a distinguishing recruiting dynamic compared to purely software-based AI companies.
Given the company's relatively recent founding and rapid scaling, alumni tenure is generally short, and most people leaving are doing so after a specific completed program or milestone rather than after a long multi-year run.
Org structure and titles
The company runs distinct engineering tracks for mechanical/hardware engineering, electrical/controls engineering, and AI/software (including robot learning and perception), each with its own Senior/Staff/Principal-style ladder consistent with hardware-company norms rather than pure software-company norms.
Given the physical product, program and project management roles carry more weight here than at pure software AI companies, since coordinating hardware development timelines requires a different discipline than shipping software releases.
Hiring bar and process, as commonly reported
Candidates commonly describe a process tailored to discipline: mechanical and controls engineers face deep technical screens on physical systems design and control theory, while AI/robot-learning candidates face ML-focused technical screens with an emphasis on real-world, noisy-data robustness rather than clean benchmark performance.
The bar is commonly described as high on cross-disciplinary fluency, since even AI-focused candidates are expected to understand hardware constraints, and hardware-focused candidates are expected to understand how their systems interface with modern AI perception and control stacks.
Compensation posture
| Level / Function | Typical total comp range | Equity form | Notes |
|---|---|---|---|
| Mechanical/hardware engineer, senior | $220K–$350K | Equity (pre-IPO) | Base-heavier than pure AI software comp; hardware engineering norms apply. |
| Controls/electrical engineer, senior | $230K–$380K | Equity (pre-IPO) | Scarcity premium for robotics-specific controls experience. |
| AI/robot learning engineer | $280K–$450K | Equity (pre-IPO) | Comp increasingly benchmarked against software AI labs given cross-industry demand. |
| Program/project management | $180K–$300K | Equity (pre-IPO) | Hardware program management experience commands a premium given scarcity. |
Directional ranges; hardware-adjacent roles generally carry a smaller equity multiple relative to base than pure AI software roles at comparable seniority.
How Figure AI alumni transfer
| Origin team | Strengths they bring | Best-fit destination | Watch-outs |
|---|---|---|---|
| Embodied AI/robot learning | Applying foundation-model techniques to noisy, real-world robot control | Robotics and physical-AI startups across any vertical | Small global talent pool; expect competing offers from multiple robotics companies simultaneously. |
| Mechanical/hardware engineering | Humanoid robot design and manufacturing at working-prototype-to-production stages | Robotics, hardware, and advanced manufacturing companies | Longer hiring and ramp cycles than software roles; plan search timelines accordingly. |
| Controls/electrical engineering | Real-time control systems for complex, dynamic physical systems | Robotics, automotive, and aerospace/defense companies | Skills transfer well across robotics verticals but require domain-specific ramp time. |
How to recruit out of Figure AI
Given the small global pool of humanoid robotics talent, candidates here are commonly fielding interest from multiple robotics and physical-AI companies at once; a search should expect a competitive process and be prepared to move faster than a typical software search timeline would suggest.
Motivation for a move commonly centers on wanting to work on a different embodiment or application domain (industrial versus consumer versus defense-adjacent robotics) or on program timeline frustration common to ambitious hardware roadmaps. Counter-offers here often include accelerated program ownership rather than pure comp increases, since hardware companies frequently retain talent through project assignment changes.
Frequently asked questions
Is it hard to hire robotics engineers away from Figure AI?
Yes, given the small global pool of humanoid robotics talent and multiple competing companies in the space. Expect a fast, competitive process and be ready to move quickly once a strong candidate is engaged.
What kind of engineers work at Figure AI?
A cross-disciplinary mix of mechanical, electrical/controls, and AI/robot-learning engineers, since building humanoid robots requires deep hardware expertise alongside modern AI perception and control skills.
Do Figure AI hardware engineers transfer to non-robotics companies?
Rarely as a direct fit. Their strongest transfer is to other robotics, advanced manufacturing, automotive, or aerospace/defense companies where hardware and controls expertise applies directly.
How does hiring speed differ for robotics roles versus software AI roles?
Hardware and controls hiring cycles are generally longer, with more emphasis on physical systems design experience; ramp time after hire is also longer than for a comparable software role, and search timelines should account for that.
What motivates a Figure AI engineer to consider a move?
Commonly, interest in a different embodiment or application domain (industrial, consumer, or defense-adjacent robotics), or frustration with hardware program timelines, more than pure compensation dissatisfaction.