As a Software Engineer at Kodiak, you play a vital role in building the artificial intelligence and advanced robotics infrastructure that powers autonomous commercial trucking and defense applications. This position directly shapes how fully-loaded autonomous semi-trucks navigate complex environments safely, efficiently, and reliably across public highways. Your daily contributions impact core software domains ranging from motion planning and vehicle controls to routing, localization, and mission planning infrastructure. The work combines fundamental theoretical robotics with high-stakes production deployment, requiring you to write robust, safety-critical code that operates reliably on heavy-duty vehicle platforms. You will collaborate closely with multidisciplinary teams including hardware engineers, reliability scientists, system architects, and test engineers to validate real-world physical performance against rigorous safety standards. Whether you are tuning feedback loops for vehicle actuation or scaling distributed infrastructure for autonomous routing, your work is central to delivering the future of autonomous ground transportation. Success at Kodiak requires a blend of rigorous analytical thinking, rigorous safety awareness, and pragmatic software engineering. You will be challenged to solve novel problems in autonomous driving while maintaining an unwavering focus on real-world reliability and passenger and public safety.
Recruiter Screen
reportedInitial discussion about your background, interests, and alignment with the role.
What to demonstrate
- Initial discussion about your background, interests, and alignment with the role
- Depth in C++
How to prepare
- Be able to walk your CV end to end in two minutes, and say why this company specifically.
- Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.
Technical Phone Screens
reportedOne or two technical interviews focusing on coding, system design, or domain-specific problem-solving.
What to demonstrate
- One or two technical interviews focusing on coding, system design, or domain-specific problem-solving
- Depth in C++
How to prepare
- Be able to walk your CV end to end in two minutes, and say why this company specifically.
- Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.
Comprehensive Loop
reportedVirtual or onsite interviews with multiple members of the engineering, validation, and safety teams.
What to demonstrate
- Virtual or onsite interviews with multiple members of the engineering, validation, and safety teams
- Depth in C++
How to prepare
- Answer aloud and timed: What strategies do you use for formal verification and writing strict requirements in safety-critical domains?
- Answer aloud and timed: How would you design a scalable mission planning infrastructure for cross-country autonomous commercial trucking?
PracHub editorial advice for the preparation topics above.
Ground your answers in safety
Always frame your technical decisions around fail-safe design, redundancy, and real-world reliability. At Kodiak, safety is never an afterthought.
Clarify assumptions early
When presented with open-ended system design or coding problems, proactively state your assumptions regarding hardware constraints, latency limits, and failure modes before diving into solutions.
Master your C++ fundamentals
Expect your code to be scrutinized for efficiency, memory safety, and readability. Practice writing clean, compilable code without relying entirely on IDE autocomplete.
Communicate your trade-offs
Interviewers value engineers who understand that every architectural choice involves compromise. Explicitly discuss the pros and cons of your proposed algorithms or system designs.
Highlight cross-functional teamwork
Be prepared to share specific stories about how you collaborated with hardware, testing, or product teams to resolve difficult integration challenges.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Who do you look up to for solutions when facing an ambiguous, unprecedented robotics or software challenge?
Who do you look up to for solutions when facing an ambiguous, unprecedented robotics or software challenge?
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Diff a projection against the primary without per-row point reads
The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.
Approach
- Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
- Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
- For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
- Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.
Follow-up
- The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
- Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?
Merge partitioned event streams into one ordered feed with bounded lateness
The read-model service consumes 64 log partitions carrying about 4,000 events per second in total. Each partition is ordered within itself, but partitions drift by up to 30 seconds, and the activity feed must present a tenant's events in occurred_at order. Produce the merge. State its complexity, the buffer it requires in events and in bytes, what happens when one partition is idle, and what you do with an event that arrives after you have already emitted its position. Payloads average 1 KB.
Approach
- Merge with a min-heap over the 64 partition heads keyed on (occurred_at, event_id): O(log P) per event and O(n log P) overall. The tie-break on event_id is what makes the output deterministic when two partitions carry the same millisecond, which matters because the feed is paginated and a non-deterministic order reorders pages under the reader.
- Emitting the heap head is only correct once every partition has produced everything up to that timestamp, so the emit condition is a watermark: the minimum across partitions of the highest occurred_at seen, less the allowed lateness. Events are held until the watermark passes them, which is what turns individually ordered streams into a jointly ordered one.
- Size the buffer from the lateness rather than guessing: 4,000 events per second times 30 seconds is 120,000 buffered events, and at 1 KB each about 120 MB of heap. That number is the real price of the ordering guarantee and belongs in front of whoever asked for it.
- Handle the idle partition explicitly, because it fails the feed rather than corrupting it: a partition with no traffic never advances its own maximum, so the watermark freezes and output stops entirely. Either every partition emits a periodic idle marker carrying the broker's current time, or the watermark falls back to wall clock for a partition silent beyond a threshold.
Follow-up
- The lateness budget is raised to five minutes. What is the new buffer, and what besides memory changes?
- The consumer restarts. Where does it resume from, and what does the feed look like for the first 30 seconds?
Denormalise tenant onto revisions and backfill it live
resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.
Approach
- Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
- Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
- Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
- Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.
Follow-up
- The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
- How do you verify the backfill actually finished, given rows are still being inserted while it runs?
Keep soft-deleted accounts from blocking re-registration
app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.
Approach
- Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
- Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
- For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
- State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.
Follow-up
- A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
- How do you honour an erasure request while resource_revision.actor_user_id still references this table?
What foundational metrics and simulation tools do you use when tuning feedback controllers for heavy mobile ro
What foundational metrics and simulation tools do you use when tuning feedback controllers for heavy mobile robots?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How do you approach system identification and vehicle modeling for complex dynamics like steering and braking?
How do you approach system identification and vehicle modeling for complex dynamics like steering and braking?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Can you describe how you would architect redundant actuation pathways for a safety-critical autonomous system?
Can you describe how you would architect redundant actuation pathways for a safety-critical autonomous system?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
What strategies do you use for formal verification and writing strict requirements in safety-critical domains?
What strategies do you use for formal verification and writing strict requirements in safety-critical domains?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How would you design a scalable mission planning infrastructure for cross-country autonomous commercial trucki
How would you design a scalable mission planning infrastructure for cross-country autonomous commercial trucking?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Walk through how you would architect a low-latency communication pipeline using protocols like CAN or Ethernet
Walk through how you would architect a low-latency communication pipeline using protocols like CAN or Ethernet for sensor-to-actuator data flow.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
How do you ensure high availability and graceful degraded operation when core localization or routing modules
How do you ensure high availability and graceful degraded operation when core localization or routing modules fail?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Design a testing framework for validating hardware-in-the-loop and software-in-the-loop autonomous driving sce
Design a testing framework for validating hardware-in-the-loop and software-in-the-loop autonomous driving scenarios.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
How do you structure software components to cleanly separate high-level trajectory generation from low-level v
How do you structure software components to cleanly separate high-level trajectory generation from low-level vehicle controls?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Read latency spikes on a sixty-second sawtooth
The cached listing read path serves about 14k reads/second at an 85% hit rate. p99 sits at 35 ms for 57 seconds, jumps to 900 ms for 3, and repeats. During each spike the primary shows several hundred identical listing queries starting within the same millisecond, all carrying one large tenant's id. Cache entries use a 60-second TTL. Give the mechanism, the ordered checks, the fix, and the correctness hazard your fix must not introduce.
Approach
- Match the period to a configured number before theorising about load. A spike every 60 seconds against a 60-second TTL is an entry expiring, and you confirm it by correlating spike timestamps with the entry's write time rather than with the traffic curve. If the period had matched a cron or a GC interval instead, this is a different investigation.
- Establish the concurrency of the miss. Several hundred identical queries in one millisecond means the miss path has no coalescing: every request that arrives between expiry and repopulation recomputes. The herd size is that key's arrival rate times its recompute time, so at 1.2k reads/second for the hot key and a 250 ms recompute you expect about 300 concurrent misses, which matches what is observed.
- Add single-flight on the miss path so one caller per key recomputes under a short-lived lock while the rest wait for its result. Prefer stale-while-revalidate where the read tolerates it: return the expired value immediately and refresh asynchronously, which removes the latency spike rather than serialising it into a queue of waiters.
- De-synchronise the keys. Write TTLs with jitter, for example 60 seconds plus or minus 10%, so a deploy or a mass invalidation does not align every key on the same second and turn a per-key herd into a fleet-wide one.
Follow-up
- The same sawtooth appears on a key that is invalidated on write rather than expired. Is that the same bug?
- How does your answer change if the recompute takes 4 seconds instead of 250 ms?
Built from the rounds and topics Kodiak candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Kodiak loop
- Write out the reported sequence: Recruiter Screen, Technical Phone Screens, Comprehensive Loop.
- For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.
Deliverable: A one-page map of the 3 reported rounds, with the weakest marked.
02Work C++
- Spend the session on C++, which Kodiak candidates report being tested on.
- Write one worked example in C++ and time yourself on it.
Deliverable: One timed worked example in C++.
03Work Safety-critical software
- Spend the session on Safety-critical software, which Kodiak candidates report being tested on.
- Write one worked example in Safety-critical software and time yourself on it.
Deliverable: One timed worked example in Safety-critical software.
04Work Feedback control systems
- Spend the session on Feedback control systems, which Kodiak candidates report being tested on.
- Write one worked example in Feedback control systems and time yourself on it.
Deliverable: One timed worked example in Feedback control systems.
05Answer out loud: Technical and Domain Expertise
- Answer aloud, timed: What foundational metrics and simulation tools do you use when tuning feedback controllers for heavy mobile robots?
- Answer aloud, timed: How do you approach system identification and vehicle modeling for complex dynamics like steering and braking?
Deliverable: Spoken answers to 2 reported Technical and Domain Expertise question(s), under time.
06Answer out loud: System Design and Architecture
- Answer aloud, timed: How would you design a scalable mission planning infrastructure for cross-country autonomous commercial trucking?
- Answer aloud, timed: Walk through how you would architect a low-latency communication pipeline using protocols like CAN or Ethernet for sensor-to-actuator data flow.
Deliverable: Spoken answers to 2 reported System Design and Architecture question(s), under time.
07Answer out loud: Behavioral and Problem-Solving
- Answer aloud, timed: Who do you look up to for solutions when facing an ambiguous, unprecedented robotics or software challenge?
- Answer aloud, timed: Tell me about a time you had to balance aggressive delivery timelines with uncompromising safety standards.
Deliverable: Spoken answers to 2 reported Behavioral and Problem-Solving question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
How do you handle resource-constrained embedded environments when writing real-time C or C++ software?
How do you handle resource-constrained embedded environments when writing real-time C or C++ software?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Tell me about a time you had to balance aggressive delivery timelines with uncompromising safety standards.
Tell me about a time you had to balance aggressive delivery timelines with uncompromising safety standards.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Describe a situation where a safety-critical bug slipped past initial testing. How did you isolate it and prev
Describe a situation where a safety-critical bug slipped past initial testing. How did you isolate it and prevent recurrence?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
How do you manage technical disagreements when collaborating across hardware and software engineering teams?
How do you manage technical disagreements when collaborating across hardware and software engineering teams?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Give an example of how you mentored junior engineers or drove architectural alignment across a multidisciplina
Give an example of how you mentored junior engineers or drove architectural alignment across a multidisciplinary team.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
- 01
How do you handle resource-constrained embedded environments when writing real-time C or C++ software?
- 02
Tell me about a time you had to balance aggressive delivery timelines with uncompromising safety standards.
- 03
Describe a situation where a safety-critical bug slipped past initial testing. How did you isolate it and prevent recurrence?
- 04
How do you manage technical disagreements when collaborating across hardware and software engineering teams?
How difficult are the technical interviews at Kodiak?
The interviews are rigorous and demand both strong theoretical understanding and practical implementation skills. Interviewers focus heavily on your ability to write clean C/C++ code and reason through complex safety-critical edge cases.
Kodiak Software Engineer candidate reports ↗How much preparation time should I plan for?
Most candidates benefit from 4 to 6 weeks of dedicated preparation, focusing on C++ proficiency, robotics fundamentals, system design, and reviewing your past project experiences through a safety lens.
Kodiak Software Engineer candidate reports ↗What differentiates successful candidates from others?
Successful candidates distinguish themselves by consistently connecting their code and algorithms to physical reality and real-world safety implications. They communicate their trade-offs clearly and demonstrate a collaborative, structured approach to problem-solving.
Kodiak Software Engineer candidate reports ↗What is the typical interview timeline from initial screen to offer?
The process typically moves efficiently over a few weeks, depending on scheduling and team alignment, starting with an initial recruiter screen and progressing through technical rounds and a comprehensive onsite loop.
Kodiak Software Engineer candidate reports ↗Are remote work options available for engineering roles?
While certain roles may offer hybrid flexibility, many engineering positions are centered around Kodiak's hubs in Mountain View and San Francisco, CA, where engineers can closely interface with physical vehicle fleets and hardware test environments.
Kodiak Software Engineer candidate reports ↗What topics does Kodiak test in interviews?
Kodiak interviews most often cover Requirements Traceability, C++, Software Quality Assurance (QA), Machine Learning (ML), and Assisted Autonomy. The exact emphasis depends on the specific role you apply for.
Kodiak Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01Kodiak Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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