# Field AI interview guide for robotics engineers

Source: https://www.techinterview.org/post/3233477938/fieldai-interview-guide/
Updated: 2026-11-11 · techinterview.org

Field AI does not publish its interview loop, so treat any round-by-round script you see as unverified. For a physical-AI startup its size, the realistic shape is a recruiter screen, one or two technical screens, and an onsite over roughly three to five weeks, all inferred rather than company-stated. The signal is embodied-autonomy depth, perception and mapping, planning and control, locomotion or manipulation, sim-to-real, plus the judgment to make a robot work in the field, not algorithm trivia. Most engineering roles sit in Irvine, California, with a research hub in Boston, and some roles gate on US citizenship. Prep for a live technical problem-solving session and a project deep-dive rather than a long algorithmic take-home.

Field AI builds the autonomy software, not the robot. Its product, Field Foundation Models, is pitched by the company as [risk-aware and embodiment-agnostic](https://www.ocbj.com/technology/fieldai-raises-405m-for-2b-valuation/): one autonomy stack meant to run a legged robot, a wheeled industrial platform, or a humanoid rather than a model hand-tuned for each machine. The Orange County Business Journal described it in 2025 as AI software for other companies' robots, though its job board now also lists robotics-hardware roles. The company was incorporated in 2023 and raised $405 million across two rounds announced in August 2025, which lifted its valuation to about $2 billion, per that same OCBJ reporting. In early October 2026, Business Insider reported it had signed a term sheet for a [$700 million round at a $10 billion valuation](https://runtimewire.com/article/fieldai-700m-term-sheet-10b-valuation); as of this writing that round has not been reported as closed. That direction, a general robot brain rather than a single product, shapes what the interview digs for.

## The leadership's research pedigree raises the bar

Field AI's leadership comes out of hard-robotics research, and that shows up in the questions. Per the company's [team page](https://fieldai.com/team), Ali Agha spent seven years at NASA's Jet Propulsion Laboratory and led the CoSTAR team that competed in the DARPA Subterranean Challenge, the contest where robots had to explore tunnels and caverns with no map and patchy communication. David Fan, also ex-JPL, was a chief engineer on the DARPA SubT and RACER programs. Shayegan Omidshafiei spent five-plus years as a research scientist at Google and DeepMind, and Sebastian Scherer is an associate research professor at Carnegie Mellon's Robotics Institute. The common thread is autonomy in places where the environment fights back.

If you want to prep against their actual priors, the cheapest study is their published work: the CoSTAR team's NeBula papers from the DARPA Subterranean Challenge, the RACER program writeups on high-speed off-road autonomy, and Scherer's group at CMU's Robotics Institute. Knowing that lineage lets you talk about uncertainty and field autonomy in their terms. The team respects people who have made a real robot do something in an uncontrolled setting, and they can tell that apart from a clean benchmark result. If your experience is a simulated demo or a Kaggle score, expect to be pushed on what breaks when the sensor is dirty, the floor is uneven, and the thing has to recover on its own. Field AI sits in the same physical-AI cohort as [Physical Intelligence](/companies/physical-intelligence-interview-guide/), [Skild AI](/companies/skild-ai-interview-guide/), and [Generalist](/post/3233477426/generalist-interview-guide/), all chasing a cross-embodiment foundation model, so the bar for robotics fundamentals tracks those.

## The interview loop Field AI doesn't publish

There is no engineering blog post or public rubric describing the loop, and the Glassdoor sample for its autonomy roles is too thin to read as a pattern. So the table below is an inferred shape for a venture-backed robotics company of this size, cross-checked against third-party interview guides and one anonymous Glassdoor report (recruiter screen, technical screen, onsite, with long gaps between stages). Treat the stage count, order, and timeline as approximate, not reported.

On format, infer rather than assume. For autonomy and research roles the technical screen most likely runs as a live problem-solving session in C++ or Python with a robotics flavor, not a long take-home, followed by a deep-dive on a past project you actually shipped. An onsite at this kind of company usually mixes applied system design (sketch a perception or planning pipeline on a whiteboard) with a live-coding round and a round on field-deployment judgment. That shape is an inference consistent with how comparable physical-AI loops read, not a published Field AI script.

| Field AI interview and company facts (as of November 2026) | Detail | How well-sourced |
| --- | --- | --- |
| Founded / HQ | 2023; Irvine, California (state records list Mission Viejo), with a Boston research hub | Confirmed (company, press) |
| Product | Field Foundation Models: risk-aware, embodiment-agnostic autonomy software; described in 2025 as software for other companies' robots, though the job board now lists hardware roles too | Confirmed (company; OCBJ) |
| 2025 funding | $405M across two rounds, ~$2B valuation; backers include Bezos Expeditions, Khosla Ventures, Emerson Collective, Temasek, Intel Capital, NVentures, plus Bill Gates | Reported (OCBJ, Aug 2025) |
| 2026 funding | $700M at $10B valuation | Reported term sheet (Business Insider, Oct 2026); not reported as closed |
| Interview loop | Recruiter screen, one to two technical screens, onsite; roughly 3 to 5 weeks | Inferred from third-party reports, not company-published |
| Where engineering sits | Mostly Irvine, CA; research roles in Boston; some roles in Peoria IL, Seattle, Pittsburgh; forward-deployed roles in Korea, Japan, Singapore | Confirmed (Lever job board) |
| Compensation | One forward-deployed (customer-facing, possibly non-US) role posts $100K-$140K base, which is not a read on a core Irvine autonomy or research role; a 2026 market band for senior robotics/ML engineers at well-funded US startups is roughly $180K-$260K base plus equity, a figure to verify on levels.fyi/LCA rather than a Field AI number; no Levels.fyi page or public LCA data for Field AI found | Partial (Lever posting + market reference) |

The practical read: ask your recruiter for the exact stages up front, because they vary by role family and the public record won't tell you. This is a case where the [AI-startup interview difficulty index](/ai-startup-interview-difficulty-index/) and the broader [AI-native company interview guides](/ai-startup-interview-guides/) are more useful for calibrating the bar than any single Field AI review.

## Robotics and ML questions the work points to

Field AI's [job board](https://jobs.lever.co/field-ai) is unusually legible about the work. Roles cluster into perception and mapping (grouped as calibration, localization, and mapping), planning and control, locomotion and manipulation, humanoid whole-body learning, simulation and sim-to-real, and a vision-language-reasoning layer on top. Match your prep to the family you're applying to, and expect depth over breadth. The common grading theme is uncertainty: the word "risk-aware" in the company's own product description is a strong hint they want engineers who treat failure as the main case, not the happy path.

The kind of thing that gets asked, phrased the way it tends to come up and grouped by the family it maps to:

- Perception and mapping: your lidar and visual odometry disagree on the robot's position halfway through a run. How do you fuse them, and how does the system decide it has lost localization rather than trust a bad estimate?

- Planning and control: a corridor you planned through is now blocked. Walk through how you replan live, and where you'd draw the line between a fast reactive controller and a slower global planner.

- Locomotion and manipulation: a legged robot has to cross uneven ground it has never seen, or an arm has to grasp a partly occluded object. What's the recovery behavior when the first attempt slips?

- Sim-to-real and learned behavior: a policy you trained in simulation degrades on hardware. How do you diagnose the sim-to-real gap, and how do you evaluate the policy against known-correct outcomes instead of a cherry-picked demo?

If you're coming from the ML side, the depth here sits closer to the [AI-era interviewing](/ai-era-interview-guide/) bar than a generic LeetCode round. Several roles name vision-language reasoning, multimodal agents, and mission evaluation, and a strong answer covers how you keep a human able to audit and override an autonomous decision rather than trusting a model end to end.

## Systems, deployment, and the forward-deployed reality

A robot brain runs on hardware, in the field, often disconnected, so a chunk of the loop is a systems conversation. The forward-deployed engineer roles make it explicit: Field AI sends people to a customer site to stand up the stack on a real machine in an environment nobody controlled. ROS shows up by name in at least one senior posting on the board, so if you claim it, know the real cost model, not the tutorial version: message-passing latency, why a naive node graph starves under load, and how you'd debug a dropped topic on live hardware.

Questions in this part of the loop tend to look like:

- Forward-deployed systems: you're running inference on an embedded platform with a fixed compute and power budget and an intermittent network. How do you keep the control loop deterministic and degrade safely when a sensor drops out?

- Safety and validation: how would you catch a regression in an autonomy stack before it reaches a robot in the field, and what does your test and validation setup look like for something that moves in the physical world?

Safety recurs across the board, with dedicated functional-safety and validation roles, which maps to how autonomy companies grade elsewhere; the [Waymo safety round](/post/3233476865/waymo-interview-loop-safety-round/) and the [Applied Intuition](/post/3233477460/applied-intuition-interview-guide/) guide show how that conversation runs. One practical wrinkle for non-citizens: Field AI runs a Federal arm on wheeled autonomous platforms, and that work, plus some overseas forward-deployed roles, can gate on US citizenship or a security clearance, the same deployed-government-robotics constraint you'd see at [Mach Industries](/companies/mach-industries-interview-guide/). Ask early where a given req stands.

## Compensation and what to ask before you commit

The public comp picture is thin. No Levels.fyi page for Field AI turned up and no H-1B LCA disclosure data surfaced. Individual Lever postings do list base ranges, but read them in context: one forward-deployed engineering role, a customer-facing and possibly non-US position, posts $100,000 to $140,000, which is not a read on what a core autonomy or research engineer in Irvine makes. As a 2026 market reference, senior robotics and ML engineers at well-funded US startups tend to land in the rough neighborhood of $180,000 to $260,000 base plus meaningful equity; treat that as a market band to verify, not a Field AI figure. Check it against levels.fyi entries for peer robotics startups and, once the company sponsors visas at scale, the public DOL LCA filings. On sponsorship itself the company states no policy publicly, so ask the recruiter directly, and non-citizens should confirm it before counting on the Federal or overseas roles.

The part that actually moves the number is equity, and at a company whose valuation has reportedly jumped roughly fivefold in a year, the strike price and preferred valuation your grant is set against matter more than base. Get the specifics in writing: option or RSU count, current preferred valuation, strike price, vesting schedule, and whether there's a cliff. Run the result through a [total comp calculator](/total-comp-calculator/) before comparing offers, and if you hold competing ones, the usual [salary negotiation](/post/3233474669/salary-negotiation-2026/) playbook applies even at an early-stage robotics shop. The plain framing: you're partly betting on the term sheet closing and the valuation holding, so size the equity to that risk rather than to the headline number.