Rivian interviews depend heavily on the team. A backend engineer building fleet services, an embedded engineer working on the vehicle, and a data scientist working with telemetry will each get a different loop. What they share is a vehicle company’s mix: standard coding questions, questions about reading messy sensor data, and some knowledge of how electric vehicles work. This page covers the questions worth practicing, with working code for the software ones.
Rivian does not publish its interview format, so the process below comes from the general pattern at tech and automotive companies and from what candidates report. Ask your recruiter what each round covers.
About the company, briefly
Rivian was founded in 2009 by RJ Scaringe. Its consumer vehicles are the R1T pickup and R1S SUV, and it builds electric delivery vans for Amazon at its plant in Normal, Illinois. As of October 2026 it has also started delivering the smaller R2 SUV, and it has said part of its spending goes into in-house driver-assistance and autonomy software. Read its newsroom before the interview, because interviewers like candidates who know what the product team is shipping.
The usual process
- A recruiter conversation about your background and the role.
- A technical screen with an engineer: live coding for software roles, a technical discussion for hardware or data roles.
- A loop of interviews with engineers, a manager, and sometimes people from neighboring teams.
- For senior roles, a design discussion.
Software engineering questions
Fundamentals questions you should be able to answer in two minutes each:
- Stack versus heap memory: what lives where, and what goes wrong when you get it wrong (stack overflow, leaks, dangling pointers).
- Processes versus threads: isolation versus shared memory, and what a race condition looks like.
- How a hash table handles collisions: chaining versus open addressing, and what resizing costs.
- Quicksort: average O(n log n), worst case O(n^2), and how pivot choice affects that.
- What the SOLID principles are, with one concrete example of breaking one.
If the role is embedded or close to the vehicle, add: C or C++ basics, interrupts, what a CAN bus is, and how you would test code that talks to hardware.
Coding problems with tested answers
Reverse a linked list
Walk the list once and flip each pointer. Three variables are enough: the node you are on, the one before it, and (implicitly) the one after it.
class ListNode:
def __init__(self, val=0, next=None):
self.val, self.next = val, next
def reverse_list(head):
prev = None
while head:
head.next, prev, head = prev, head, head.next
return prev
def to_list(h):
out = []
while h:
out.append(h.val)
h = h.next
return out
h = ListNode(1, ListNode(2, ListNode(3)))
print(to_list(reverse_list(h))) # [3, 2, 1]
O(n) time, O(1) space. The tuple assignment evaluates the right side first, so it is safe, but in an interview say it out loud or use a temporary variable if the interviewer seems unsure. Practice it in our Reverse Linked List lesson.
K-th largest element
Sorting works at O(n log n), but a min-heap of size k does it in O(n log k): keep the k largest values seen so far, and the smallest of those is the answer.
import heapq
def kth_largest(nums, k):
heap = []
for x in nums:
heapq.heappush(heap, x)
if len(heap) > k:
heapq.heappop(heap)
return heap[0]
print(kth_largest([3, 2, 1, 5, 6, 4], 2)) # 5
Mention quickselect as the O(n) average-case alternative, and why it is worse when you need a stream of answers (the heap handles new values naturally).
Longest palindromic substring
Expand around each center, once for odd length and once for even length. O(n^2) time and O(1) extra space, and much easier to write correctly than dynamic programming.
def longest_palindrome(s):
best = ""
for i in range(len(s)):
for l, r in ((i, i), (i, i + 1)):
while l >= 0 and r < len(s) and s[l] == s[r]:
l -= 1
r += 1
if r - l - 1 > len(best):
best = s[l + 1:r]
return best
print(longest_palindrome("babad"), longest_palindrome("cbbd")) # bab bb
For “babad”, “aba” is also a correct answer; say so, because the problem usually accepts any longest one.
Vehicle-flavored: rolling maximum of sensor readings
A question that fits a company with a telemetry stream: given battery temperature readings arriving one at a time, report the maximum over the last k readings after each new one. Rescanning k values each time costs O(n k). A monotonic deque keeps candidates in decreasing order, so each reading is pushed and popped at most once.
from collections import deque
class RollingMax:
def __init__(self, size):
self.size, self.i, self.dq = size, 0, deque()
def add(self, x):
while self.dq and self.dq[-1][1] <= x:
self.dq.pop()
self.dq.append((self.i, x))
if self.dq[0][0] <= self.i - self.size:
self.dq.popleft()
self.i += 1
return self.dq[0][1]
r = RollingMax(3)
print([r.add(x) for x in [31, 35, 33, 32, 30, 29, 40]])
# [31, 35, 35, 35, 33, 32, 40]
This is the sliding window idea; our Sliding Window Technique lesson is a gentler introduction to it.
System design questions
Prompts that match what a vehicle company builds:
- Collect and analyze telemetry from a large fleet of vehicles.
- Deliver over-the-air software updates safely.
- Combine route planning with charger availability and traffic.
- Monitor battery health over time and flag anomalies.
What makes these different from a web-app design question: vehicles connect and disconnect, links are slow or expensive, data arrives late and out of order, and a bad update is a physical safety problem, not just a bug. For OTA updates, talk about staged rollouts, signed images, resumable downloads, a way to roll back, and verifying the update before switching to it. For telemetry, talk about batching on the vehicle, idempotent ingestion, and what you do with readings that arrive hours late.
General method: clarify requirements, sketch the main flow, go deep on one or two parts, discuss failure. See our system design concepts post for the building blocks.
Data science and electrical engineering questions
Reported topics for data roles: supervised versus unsupervised learning, L1 versus L2 regularization (L1 pushes weights to exactly zero, so it also selects features), imbalanced classification (resampling, class weights, and picking metrics such as precision and recall instead of accuracy), and the curse of dimensionality.
For electrical and powertrain roles: AC versus DC motors, what the inverter does (turns the battery’s DC into AC for the motor), regenerative braking (the motor acts as a generator and sends energy back to the battery), and battery thermal management (why cells need to stay within a temperature range for performance and safety).
EV knowledge questions
You will not be quizzed like a trivia show, but you should have an opinion on a few things: why range varies with temperature and speed, the trade-offs between battery chemistries, how charging connector standards differ in your region (they have been changing in North America, so check what is current), and what vehicle-to-grid means. Read recent industry coverage and Rivian’s own announcements, and form your own view.
Behavioral questions
Typical ones: working to a tight deadline, handling a disagreement, learning something new quickly, a mistake you made and what you changed afterwards. Use the Situation, Task, Action, Result structure, and prepare stories where you can describe the technical details. Hardware companies value people who say “this failed, here is how we found out why”.
Preparation checklist
- Do 30 to 40 standard problems across arrays, hash maps, linked lists, trees and heaps, and explain each one aloud.
- Write the four solutions above from memory.
- Refresh the fundamentals list for your role (OS and memory for software, signal and power basics for hardware).
- Prepare four stories for behavioral questions.
- Read about the R1, R2 and commercial van programs, and have two questions ready for the interviewers.
For hands-on practice, AlgoCademy offers interactive lessons where you write the code and get feedback step by step; LeetCode and HackerRank have larger problem banks.
