Anduril Coding Interview Questions: Autonomy, Sensor Fusion, and Edge Software

Anduril is a defense technology company that builds both hardware and the software that runs on and connects it, and its engineering roles typically span autonomy, perception, robotics, embedded and edge software, and the platform and infrastructure work that ties systems together. Candidates commonly describe interviews that begin with standard coding and then move into the practical questions that come with software running on physical systems: sensors that disagree, clocks that drift, limited compute, and network links you cannot count on.

This guide covers eligibility, how the loop is commonly described, the autonomy and sensor-fusion fundamentals worth reviewing, what changes when software runs at the edge, the types of problems reported most often, the mission-alignment conversation, and a three-phase plan. It stays on interview preparation and describes problem categories, not leaked prompts; question sets rotate, and a solid grasp of the underlying ideas is what carries from one interview to the next.

Eligibility: clearance and citizenship requirements

Some Anduril roles involve a security clearance, U.S. citizenship, or similar eligibility requirements, and others may not. Postings typically spell out what a given role requires, and requirements can differ from one position to another, so check each posting before you apply and raise anything unclear with your recruiter.

Nothing on this page is legal or eligibility advice. For what a specific role requires, rely on the job posting and on what your recruiter tells you.

How the Anduril loop typically runs

Candidates commonly report a shape like the one below. Round counts and ordering vary, and some teams add or skip stages, so confirm your specific schedule with your recruiter.

Stage	What happens	Focus
Recruiter call	Background, team match, eligibility questions, and early motivation	Team match and logistics
Technical screen	Live coding with an engineer, sometimes with domain follow-ups	Core skills and clarity
Hiring manager (some teams)	Your experience, how you work, and why this field	Judgment and motivation
Onsite or virtual loop	Several interviews across coding, system or domain design, and behavioral topics	Depth, teamwork, and mission fit

Candidates also describe a practical tone throughout. Even a standard algorithm question may come back with a follow-up that ties it to hardware, deployment, or operating conditions, so be ready to move from an abstract solution to the physical system it would run on.

Autonomy and sensor fusion: the fundamentals worth reviewing

Autonomy, perception, and robotics teams commonly probe the concepts below. Most roles do not require research-level depth, but you should be able to explain each idea plainly and sketch where it shows up in code.

• Coordinate frames and transforms. Converting between a sensor's frame, a vehicle's body frame, and a world frame, and composing rotations and translations in the right order.
• Time alignment. Sensors sample at different rates and stamp data with clocks that can drift, so measurements have to be matched or interpolated before they are combined.
• State estimation. The predict-and-update idea behind Kalman-style filters: carry an estimate and its uncertainty, predict it forward, then correct it with each measurement weighted by how much you trust that source.
• Data association. Deciding which new measurement corresponds to which known landmark or object, and handling measurements that match nothing.
• Planning. Graph search on grids and road-map graphs, from BFS and Dijkstra to A* with an admissible heuristic, plus the cost of replanning when the map changes.
• Control and behavior. Feedback control at a conceptual level, and behavior expressed as state machines or behavior trees that stay predictable when inputs go missing.
• Simulation and testing. Replaying logged sensor data, testing in simulation before hardware, and deciding what must still be verified on the real system.

For the search side of planning questions, our graph algorithms guide covers BFS, DFS, and topological sort in interview form. If you are weighing autonomy roles in other industries too, our Tesla interview guide covers the automotive side; this page stays on the defense technology context.

When software runs at the edge

Another theme candidates commonly describe is software that has to keep working on devices with limited compute and power, often over links that are slow, intermittent, or unavailable. That changes both coding answers and design answers:

• Resource budgets. Fixed memory and CPU headroom, predictable latency, and knowing which work can move off the device and which cannot.
• Operating while disconnected. Components that keep functioning locally when a link drops, then reconcile state when it returns.
• Bandwidth as a constraint. Prioritizing, compressing, or summarizing data when you cannot send everything, and deciding what gets dropped first.
• Graceful degradation. Continuing with reduced capability when a sensor or peer fails, and making that degraded state visible in logs and interfaces.
• Safe updates. Rolling out software to deployed devices with a rollback path, and handling version skew between nodes.
• Performance-aware C++. Avoiding allocation and copies on hot paths, understanding cache behavior, and knowing what a language feature costs.

Language expectations vary by team: autonomy and robotics work commonly involves C++ and Python, and platform teams may work in other languages, so the posting is your best guide. For the language itself, our C++ interview prep page covers smart pointers, move semantics, and the undefined behavior interviewers like to probe. At senior levels these themes commonly carry into system design, where a question about coordinating many devices that share data over an unreliable network would be a natural fit, and trade-offs around consistency, ordering, and conflict resolution matter more than raw scale.

Representative problem types across autonomy and platform teams

Candidates commonly report problems in the categories below. Practice each pattern rather than hunting for a specific prompt:

• Grid and graph search. Shortest paths on an occupancy grid or weighted graph, with follow-ups about obstacles that appear partway along the route.
• Merging timestamped streams. Align, interleave, or window measurements from sources with different rates, usually with two pointers or a heap.
• Geometry. Distances, rotations, bounding boxes, point-in-polygon tests, and conversions between coordinate frames.
• Priority and scheduling. Heaps for top-k selection, message prioritization under a bandwidth limit, or task scheduling with deadlines.
• Concurrency. Producer-consumer pipelines, thread-safe queues, and backpressure when a consumer falls behind.
• State machines and events. Device modes and transitions, with events that arrive late, duplicated, or not at all.
• System design (senior levels). Coordinating many devices over unreliable links, with local autonomy and later reconciliation.
• Core data structures and algorithms. Arrays, strings, hash maps, and trees, which remain the baseline across teams.

Here is a representative time-alignment exercise in C++: pair each sample from one sensor stream with the nearest-in-time sample from another, and drop pairs that are too far apart.

#include <cstddef>
#include <cstdint>
#include <utility>
#include <vector>

// Pair each timestamp in `a` with the nearest timestamp in `b`.
// Both inputs are sorted ascending (microseconds). Pairs more than
// max_skew apart are dropped. One pass: O(n + m) time.
std::vector<std::pair<std::size_t, std::size_t>>
align_by_time(const std::vector<std::int64_t>& a,
              const std::vector<std::int64_t>& b,
              std::int64_t max_skew) {
    auto gap = [](std::int64_t x, std::int64_t y) {
        return x > y ? x - y : y - x;          // absolute difference
    };
    std::vector<std::pair<std::size_t, std::size_t>> out;
    if (b.empty()) return out;                 // nothing to pair against
    std::size_t j = 0;
    for (std::size_t i = 0; i < a.size(); ++i) {
        while (j + 1 < b.size() && gap(b[j + 1], a[i]) <= gap(b[j], a[i]))
            ++j;                               // j never moves backward
        if (gap(b[j], a[i]) <= max_skew)
            out.emplace_back(i, j);
    }
    return out;
}

The strong answer states the complexity (a single pass, O(n + m) time for streams of length n and m, because the second index only moves forward) and then raises the questions an interviewer is waiting for: what if the two clocks are offset or drifting, should one sample be allowed to pair with several others, would interpolating between neighbors beat taking the nearest, and how much latency are you willing to add by buffering while late data arrives.

The mission-alignment conversation

Candidates commonly describe a part of the loop, sometimes a dedicated conversation and sometimes folded into behavioral rounds, about why they want to work in defense technology specifically. Interviewers generally seem to look for a considered, genuine answer rather than a rehearsed line, and given the nature of the work it is a reasonable thing for them to ask.

• Think it through beforehand. Know why this field appeals to you, which parts of the work you want to contribute to, and where your own boundaries are.
• Be specific. Connect your motivation to the engineering you want to do, such as autonomy, robotics, or reliable software at the edge, and to experience you already have.
• Be honest. Do not perform views you do not hold. A candid answer serves both sides better than a polished one, and it is reasonable to conclude the field is not right for you.
• Ask your own questions. How the team thinks about its work, how decisions get made, and how it balances shipping speed against testing.

Our guide to answering why you want to work somewhere shows how to structure that answer so it stays consistent when an interviewer probes further.

What interviewers tend to look for

• Solid fundamentals. Clean, correct code with the complexity stated, before any domain discussion.
• Moving between levels. Going from an algorithm to the hardware constraint underneath it to the system around it, without losing the thread.
• Robustness thinking. Asking what happens when a sensor drops out, a clock drifts, or a link goes quiet.
• Practical judgment. Choosing an approach that can ship and be tested on real hardware, and explaining the trade-off.
• Cross-disciplinary collaboration. Evidence that you work well with hardware, test, and systems engineers.
• Considered motivation. A reason for choosing this field that is specific and consistent.

A note on integrity: prepare thoroughly and reason honestly in the room. Domain follow-ups about clocks, uncertainty, and failure move past memorized answers quickly, and interviewers who build these systems notice the difference.

A three-phase prep plan

1. Phase 1, fundamentals (about a week): Arrays, strings, hash maps, two pointers, heaps, and graph traversal in C++ or Python. Say the complexity out loud on every problem.
2. Phase 2, your domain layer (about a week): Match it to your team. Autonomy and perception: frames and transforms, time alignment, state estimation, and planning with BFS, Dijkstra, and A*. Edge and embedded: resource budgets, concurrency, and performance-aware C++. Platform: system design for devices on unreliable networks.
3. Phase 3, rehearsal (a few days): Implement the time-alignment exercise and a grid planner from scratch, then run timed solo mocks that end with robustness follow-ups. Prepare your motivation answer and two ownership stories, and deliver them out loud until they sound like you.

FAQ

What is the Anduril software engineer interview process like?

Reported processes usually start with a recruiter conversation and a live coding screen, sometimes add a hiring manager conversation, and finish with an onsite or virtual loop of several interviews covering coding, system or domain design, and behavioral topics including motivation. Round counts and ordering differ by team and level and shift over time, so ask your recruiter to confirm the details of your own loop.

How hard are Anduril coding interview questions?

Candidates typically describe standard data structures and algorithms questions around the medium level, often with a practical framing such as timestamped sensor data, grid search, geometry, or concurrency. The harder part tends to be the follow-up discussion about real-world conditions like clock drift, limited compute, and unreliable networks. Difficulty varies by team and level.

Do I need robotics or sensor-fusion experience to interview at Anduril?

Not for every role. Autonomy, perception, and robotics teams commonly probe coordinate frames, time alignment, state estimation, and planning, while platform, infrastructure, and product teams tend to look closer to a general software loop with system design. If robotics or sensor-fusion work appears on your resume, expect detailed follow-up questions about it.

What is the mission-alignment discussion at Anduril?

Candidates commonly describe being asked why they want to work in defense technology and how they think about the purpose of the work. Interviewers generally seem to value a considered, genuine answer over a rehearsed one. Reflect on your motivation beforehand, connect it to the engineering you want to do, and be honest with yourself and the interviewer about whether the field is a good fit for you.

Do Anduril roles require a security clearance or U.S. citizenship?

Some roles involve a security clearance, U.S. citizenship, or similar eligibility requirements, and others may not. Each job posting is the place to check, since requirements can differ from one position to the next, and your recruiter can clarify anything the posting leaves open. This guide covers interview preparation only and is not legal or eligibility advice.