What to Expect in the Anduril System Design Interview

Anduril's system design interview is shaped by what the company actually builds: Lattice, an AI command-and-control platform that fuses data from drones, towers, and underwater vehicles into a live operational picture, running partly at the tactical edge where connectivity is intermittent, hardware is constrained, and the cost of being wrong is physical. The round covers recognizable distributed-systems territory (message queues, real-time processing, stream pipelines), but the follow-ups consistently steer toward Anduril's reality: failure modes, retries, observability, and degraded operation.

The format matches the coding rounds: prompts arrive deliberately underspecified, sometimes as an evolution of a coding problem (a standard exercise that grows into designing a radar ingestion pipeline or coordinating a drone fleet), and requirements-extraction is graded. Earlier-career candidates may get object-oriented design instead: modeling a sensor network or tasking system cleanly in code.

Reported Themes and Question Shapes

• Sensor data ingestion pipelines. Ingest high-rate telemetry from heterogeneous sensors (radar tracks, camera detections, RF hits), normalize it, and serve a fused real-time picture: the Lattice problem in miniature. Probes: out-of-order data, duplicate detections, clock skew across sensors, and backpressure when a sensor floods.
• Edge computing under intermittent connectivity. Systems that keep working when the link drops: local processing and decision-making at the edge, store-and-forward synchronization, conflict handling when partitions heal, and deciding what data is worth scarce bandwidth.
• Fleet coordination. Tasking and monitoring many autonomous assets: command distribution with acknowledgment, state reconciliation, and what an operator sees when an asset goes silent.
• Message queues and real-time processing. The classic building blocks, evaluated with unusual attention to delivery semantics: what at-least-once means when the message is a tasking command versus a telemetry point, and where idempotency must live.
• Observability as a design requirement. Not an afterthought question: interviewers care how you would know the system is degrading, especially for systems deployed where nobody can SSH in.

What Interviewers Are Probing

1. Requirements extraction from vague prompts. The opening is intentionally thin. Strong candidates interrogate the operational context first: data rates, latency tolerance (a fused track for an operator has different freshness needs than an archival log), connectivity assumptions, and, distinctively here, the cost of each failure direction: missing a real detection versus showing a false one.
2. Failure-mode fluency with physical stakes. Every component gets the question: what happens when this dies, and what does the operator experience? Designs that degrade legibly (stale data labeled stale, gaps shown as gaps, confidence surfaced) score far better than designs that fail silently. In this domain, a confident wrong picture is the worst output a system can produce.
3. Edge-first thinking. The stateless-cloud playbook partially inverts: compute sits near sensors because the link is the weakest component, sync is opportunistic, and the design question is which decisions can be made locally versus which need the wider picture. Candidates who reason naturally about this split stand out.
4. Delivery semantics with judgment. Telemetry can tolerate loss; commands cannot tolerate duplication. Interviewers reward designs that treat different data classes differently instead of applying one guarantee everywhere and paying for it.
5. Honest resource accounting. Edge hardware has real limits: power, compute, bandwidth, heat. Back-of-envelope discipline about what fits on the asset versus the ground station versus the cloud reads as domain-ready.

Walkthrough Sketch: Sensor Fusion Pipeline with a Flaky Link

Scope it through questions: five sensor types, tens of thousands of detections per second at peak, operators need a fused picture within about a second, and the site's uplink drops for minutes at a time. That last fact drives the architecture: fusion must run locally at the site, with the cloud getting an eventually-consistent replica, because an operator defending the site cannot depend on a link that disappears.

At the edge: ingestion adapters normalize each sensor's format and stamp arrival time separately from sensor time (clock skew between sensors is a given, so the fusion layer works in a common time base with per-sensor offsets estimated continuously). A local bus (an embedded broker or ring buffer, sized for the hardware) feeds a fusion engine that correlates detections into tracks, handling out-of-order arrivals with a bounded reordering window: make the window explicit, because it trades latency against completeness, and say which way you would set it and why. Backpressure: a misbehaving sensor flooding detections gets rate-limited at its adapter with the overflow counted and surfaced, never silently dropped.

Toward the cloud: store-and-forward with priorities under constrained bandwidth: track updates and health beacons first, raw sensor data last, and aged-out raw data summarized rather than shipped. When the link heals after a partition, reconciliation prefers the edge's operational truth for live state while the cloud keeps the fuller archive. Observability throughout: per-sensor liveness and quality metrics, end-to-end track latency measured at the operator's screen, and an explicit degraded-mode indicator: the interface between engineering and the human who must trust it. Close by naming the failure you optimized against: the operator seeing a confident, stale, wrong picture, and every labeled-staleness and surfaced-gap decision traces back to that.

How to Prepare

• Foundations, then the edge inversion: Grokking the System Design Interview and Grokking System Design Fundamentals for the core; Advanced System Design Interview, Volume II for the replication, consistency, and partition-handling depth these rounds reach. Then practice deliberately inverting cloud assumptions: design as if the network is the least reliable component, because at Anduril it is.
• Drill stream-processing patterns. Out-of-order handling, watermarks and reordering windows, dedup, and backpressure: the ingestion follow-ups live here.
• Rehearse the degraded-mode conversation. For every design, practice answering "the link just dropped for ten minutes: walk me through what every component and the operator sees." This single drill covers Anduril's most distinctive probing.
• Skim the domain. An hour on Lattice's public materials gives you the vocabulary (tracks, fusion, tasking, edge nodes) and lets the intentionally vague prompts snap into focus faster.

For the full loop, see What is the Anduril interview process like?, prepare the mission questions with How to answer "Why do you want to work at Anduril?", and the culture round with Top Anduril behavioral interview questions.