Figure AI Interview Preparation: Robotics Timing and a Sensor Exercise

TL;DR

• Figure AI interview preparation should match the role: robotics involves different evidence for perception, controls, embedded software and infrastructure work.
• Figure’s public careers and Helix material establish useful product context, but they do not verify a universal interview sequence or private question bank.
• Practise the timestamp exercise below to show how you reason about delayed sensor data, exact freshness boundaries and reproducible failures.
• Bring a project explanation that connects a measurement to a design decision. Label simulation results separately from real hardware results.

Start with the right Figure and the right role

This guide concerns Figure, the humanoid robotics company at figure.ai, rather than similarly named financial or design businesses. Its careers page identifies work across AI, engineering and design at its San Jose headquarters. Check the exact opening and location before adapting your preparation.

The company’s Helix overview describes a vision-language-action system connecting perception, movement and reasoning. That makes timing, evaluation and integration useful preparation themes. It does not mean every role involves training a model or writing a robot controller.

The public sources checked on September 14, 2026 did not establish a single role-independent sequence of interviews. Ask your recruiter about coding format, project presentation, hardware or simulation work, and permitted tools. The questions below are original practice, not claims about questions Figure has asked.

Use technical writing as context, not a question prediction

Figure’s original Helix technical article describes two components operating at different rates: a slower semantic component and a faster visuomotor component. The reported rates belong to that published architecture; they are not a specification for every current Figure system.

The transferable preparation question is what happens when information arrives at different times. A high-level plan can remain relevant while a low-level measurement becomes stale. A system may need to distinguish missing data, late delivery, an invalid timestamp and a valid reading that is simply too old.

For a perception role, explain how you would label data and separate training from evaluation. For a controls role, discuss feedback assumptions and the effect of delay. For infrastructure work, focus on reproducible datasets, logs and deployment rollback. For embedded work, be prepared to discuss resource limits and observability. These are role-based study suggestions, not a published Figure interview rubric.

Original exercise: select a fresh sensor reading

You receive sensor samples with two integer timestamps in milliseconds: when a sample was measured and when it became available to your program. At a decision time, select the newest measured sample that has already arrived and is no older than a specified freshness limit.

For this exercise, all timestamps use one shared clock. Each sample has a unique nonempty string ID. Arrival cannot precede measurement. If two eligible samples have the same measurement time, select the lexicographically smaller ID. Input order must not decide the result.

This is an offline reasoning exercise. It is not a robot safety controller, clock-synchronization implementation or a claim about Figure’s internal software.

Before coding, work through these samples at time 100 with a freshness limit of 20:

The selected sample is c. Choosing b would leak future knowledge into the decision, even though it is the newest measurement in the complete log.

Implement the contract before optimising it

def select_fresh_sample(samples, now_ms, max_age_ms):
    def nonnegative_int(value):
        return type(value) is int and value >= 0

    if not nonnegative_int(now_ms) or not nonnegative_int(max_age_ms):
        raise ValueError("time and freshness limit must be nonnegative integers")
    seen = set()
    best = None
    for sample in samples:
        sid = sample["id"]
        measured = sample["measured_ms"]
        arrived = sample["arrived_ms"]
        if not isinstance(sid, str) or not sid.strip() or sid in seen:
            raise ValueError("unique nonempty string id required")
        if not nonnegative_int(measured) or not nonnegative_int(arrived):
            raise ValueError("sample timestamps must be nonnegative integers")
        if arrived < measured:
            raise ValueError("arrival precedes measurement")
        seen.add(sid)
        if arrived > now_ms or now_ms - measured > max_age_ms:
            continue
        if best is None or (-measured, sid) < (-best["measured_ms"], best["id"]):
            best = sample
    return None if best is None else best["id"]

samples = [
    {"id": "a", "measured_ms": 80, "arrived_ms": 82},
    {"id": "b", "measured_ms": 95, "arrived_ms": 104},
    {"id": "c", "measured_ms": 90, "arrived_ms": 99},
    {"id": "d", "measured_ms": 70, "arrived_ms": 75},
]
assert select_fresh_sample(samples, 100, 20) == "c"
assert select_fresh_sample(samples[:1], 100, 20) == "a"
assert select_fresh_sample(samples[:1], 101, 20) is None
assert select_fresh_sample([], 100, 20) is None

The function rejects booleans as timestamps even though Python treats them as integers in some operations. It validates every row, including future arrivals, so a malformed log cannot become acceptable merely because a particular query would skip the bad row.

The scan takes O(n) time and uses O(n) memory for ID validation. Returning the ID avoids accidentally exposing a mutable dictionary as the function’s result. A large streaming system would need a different data structure and retention contract; do not claim this full-log scan is appropriate for a real-time control loop.

The follow-up questions matter as much as the function

What if no fresh sample exists?

Return a clear absence result. The caller must decide what that means for its application. A logging dashboard might show missing data; a physical system needs a separately designed and validated fallback. The selector should not invent a measurement or silently reuse an expired sample.

What if clocks are not shared?

The contract is no longer valid as written. Discuss how timestamps are established, what uncertainty is known and whether the system can compare events from different devices. Do not repair skew by subtracting an arbitrary constant until you understand how the offset was measured and how it changes. The clock skew section of the distributed systems interview questions deep dive shows how drifting clocks can put events from different machines in the wrong order.

What if you replay a recorded session?

Replay according to availability at the decision time, not just measurement time. Otherwise your test gives the algorithm information that the live system did not yet possess. Keep the replay inputs and decision policy version so a failure can be reproduced.

What if a sample is duplicated?

This exercise rejects duplicate IDs. An actual ingestion design might deduplicate exact retries while rejecting conflicting payloads. Either behavior can be discussed, but it must be specified and tested rather than emerge accidentally from container order.

Build a convincing robotics project walkthrough

Use one project to demonstrate the entire chain from observation to decision. State what you measured, how frequently, what error you expected and what outcome would count as failure. If the project ran only in simulation, say that early.

For example, imagine a simulated picker that misses objects after introducing delivery delay. A useful walkthrough shows the original timing assumption, one recorded failing sequence, and a replay that reproduces the miss. Then explain a candidate change and a test that could disprove your explanation. Simply reporting that the revised version “looks smoother” provides weak evidence.

When discussing a result, distinguish average behavior from unusual failures. A lower average delay can coexist with rare long delays. Explain which distribution or trace you inspected and why it answers the particular question. Do not invent numerical performance improvements for a project you did not measure.

Prepare to identify the boundary of your contribution. Explain which library, simulator or hardware component you used, what you changed yourself and which assumptions came from documentation. This makes the project easier to assess and gives you a reliable answer when asked to modify the design.

A focused preparation plan

First, annotate the current job description and choose two areas where you can show evidence. Next, implement the timestamp selector and test exact boundaries, delayed arrivals and shuffled input. Then rehearse a project walkthrough with a friend who interrupts with requirement changes. A live AI mock interview can add spoken follow-up questions and a written feedback report to that rehearsal.

Useful original prompts include: “How would you detect a stale measurement?”, “Which log fields let someone reproduce this failure?”, “What changes when two sensors disagree?” and “How would you test the component without moving hardware?” Answer only within the scope you know and explain how you would investigate the rest.

Use the phone-screen preparation checklist to organise recruiter questions and a short introduction. Confirm the actual assessment rules before using any assistant during an interview.

Frequently asked questions

The public sources reviewed do not establish that. Preparation should follow the opening and interview instructions. Roles across software, hardware, AI and design can require different evidence.

You do not need to claim a full reproduction to demonstrate useful reasoning. A narrow, well-explained timing or evaluation exercise can show the quality of your engineering thought. Be explicit about what you implemented and tested.

No. They are original study exercises informed by public robotics context, with no assertion of access to the company’s assessments.