Rivian Technical Interview Prep: Process, Topics and Practice

Rivian’s software interviews, as candidates describe them as of October 2026, look like a normal tech loop with a vehicle flavor: a recruiter call, a technical phone screen with one medium-difficulty coding problem, then a virtual or on-site loop of coding, system design, behavioral and hiring manager rounds. Rivian does not publish its process, and it varies by team (vehicle software, cloud, mobile, infotainment, manufacturing systems), so use this as a map, not a promise.

Stages candidates report

1. Recruiter call (about 30 minutes). Your background and logistics. Expect “why Rivian?” and be ready to say something specific about electric vehicles or the product, not generic enthusiasm.
2. Technical phone screen (about an hour). A shared editor and usually one algorithm problem of medium difficulty. Some roles get a domain task instead, such as concurrency for backend work.
3. Loop of four to five interviews. Coding, system design, behavioral, and a hiring manager conversation. Some positions add a take-home or a presentation.

Reports of total length range from one or two weeks to over a month, and candidate experiences with communication are mixed. Ask your recruiter for the schedule and the format of each round.

What to know for the coding rounds

The coding rounds are general data structures and algorithms. Prepare the standard set:

• Arrays, strings, hash maps, sets.
• Stacks, queues, heaps, intervals, sorting.
• Trees and graphs with DFS and BFS; shortest paths if you have time.
• Binary search, including searching on an answer.
• Basic dynamic programming.

Language choice depends on the team. Embedded and vehicle-side roles lean on C and C++, cloud and data roles on Python, Go or Java, and mobile roles on Kotlin and Swift. For the algorithm rounds, use the language you are fastest in unless the job description says otherwise.

What interviewers reportedly weigh is how you reason aloud and handle edge cases, not just whether the final code runs. Practice saying “my plan is…, the cost is…, the edge cases I see are…” before typing.

Practice problems with a vehicle setup

These are practice problems in the style of the domain, not Rivian’s actual questions.

1. Merge charging sessions

You have a list of (start, end) times when a charger was busy, in any order. Merge overlapping sessions to find the continuous busy periods. Sort by start, then extend the last interval when the next one overlaps.

def merge_sessions(sessions):
    out = []
    for start, end in sorted(sessions):
        if out and start <= out[-1][1]:
            out[-1][1] = max(out[-1][1], end)
        else:
            out.append([start, end])
    return out

print(merge_sessions([(9, 12), (1, 3), (2, 6), (6, 8), (10, 11)]))
# [[1, 8], [9, 12]]

Note that (2, 6) and (6, 8) touch at 6 and are merged. Ask whether touching intervals count as overlapping; the answer changes one character (<= versus <). Complexity is O(n log n) time. Also note the (10, 11) session sits inside (9, 12), so taking the max of the ends matters.

2. Maximum reading in every window of telemetry

Given sensor readings (say, battery pack temperature once per second) and a window of k seconds, report the maximum in each window. A naive scan costs O(n*k). A monotonic deque keeps indexes of candidates in decreasing order of value, so each element enters and leaves once.

from collections import deque

def window_max(readings, k):
    dq, out = deque(), []          # indexes; readings[dq[0]] is the max
    for i, x in enumerate(readings):
        while dq and readings[dq[-1]] <= x:
            dq.pop()
        dq.append(i)
        if dq[0] <= i - k:         # front index left the window
            dq.popleft()
        if i >= k - 1:
            out.append(readings[dq[0]])
    return out

print(window_max([41, 45, 43, 50, 48, 47, 52], 3))
# [45, 50, 50, 50, 52]

This runs in O(n) time. It is the same family as the problems in our sliding window lesson.

3. A battery class that rejects bad input

Small design questions are often a chance to show that you think about invalid input and invariants. This one never lets the charge leave the range 0 to 100 and does not change state when a call is refused.

#include <algorithm>
#include <iostream>

class Battery {
    double charge_ = 0.0;
    static constexpr double kMax = 100.0;
public:
    void charge(double pct) {
        if (pct < 0) return;                      // ignore bad input
        charge_ = std::min(kMax, charge_ + pct);
    }
    bool discharge(double pct) {
        if (pct < 0 || pct > charge_) return false;  // refuse, keep state
        charge_ -= pct;
        return true;
    }
    double level() const { return charge_; }
};

int main() {
    Battery b;
    b.charge(75.5);
    b.charge(40);
    std::cout << b.level() << "\n";
    std::cout << b.discharge(120) << " " << b.discharge(30) << " " << b.level() << "\n";
}
// 100
// 0 1 70

Be ready to discuss alternatives: throwing an exception versus returning a bool, and why floating-point percentages are a poor choice for real battery accounting (integers in a small unit are safer).

System design for a vehicle company

Candidates report system design prompts tied to the product: vehicle telemetry pipelines, over-the-air (OTA) update systems and charging network backends. You can prepare for them with the usual structure:

1. Clarify. How many vehicles, how often each reports, what latency is acceptable, what data is safety-relevant.
2. Estimate. Messages per second, bytes per message, storage per day. A fleet of many thousands of vehicles reporting every few seconds already means a stream-processing design.
3. Sketch the pipeline. Vehicle, ingestion endpoint, message queue, stream processor, hot store for recent data, cold storage for history, dashboards and alerts.
4. Handle the vehicle-specific problems. A car loses cellular connection in a tunnel or a rural area, so it must buffer data locally and send it later, in order and without duplicates (use message IDs). An OTA update must be resumable, verified (signatures and checksums) and roll back safely if it fails, and it must not start while the car is driving.
5. Cover reliability and security. Retries with backoff, idempotent writes, authentication of devices, encryption in transit, and monitoring.

Read our list of questions to ask in a system design interview to practice the clarifying step.

Behavioral round

Prepare stories about debugging a hard problem, working with people outside your team (hardware, manufacturing, product), owning something when requirements were unclear, and recovering from a mistake. Safety-critical and hardware-adjacent teams tend to value careful testing and clear communication, so include a story where you caught a problem early because you tested or reviewed carefully.

Preparation plan

• Weeks 1-2: core patterns (hash maps, two pointers, sliding window, intervals, stacks), two timed problems a day.
• Week 3: trees, graphs, binary search, recursion, dynamic programming basics.
• Week 4: mock interviews, two system design questions in the telemetry/OTA area, behavioral stories.
• Read the job description line by line and revise anything that shows up that you have not touched (embedded C++, Kotlin, AWS, Kubernetes, CAN bus and so on).

For more on the style of question to expect, see our Rivian interview questions post. For structured practice with explanations, AlgoCademy has interactive lessons on these topics, and you can mix in problems from LeetCode or similar sites.