As a Software Engineer at Tycho.AI, you are at the forefront of developing sophisticated autonomous systems that bridge the gap between complex perception, state estimation, and motion control. This role is not merely about writing code; it is about architecting the intelligence that allows our platforms to navigate dynamic environments with precision and reliability. You will work within a high-caliber team of engineers to solve some of the most challenging problems in robotics and AI today. Your contributions will have a direct impact on our core autonomy stack, influencing everything from real-time sensor fusion to high-level path planning. Whether you are working on Perception, Motion Planning & Controls, or State Estimation, your work will be critical to the safety and performance of our systems. We look for engineers who are passionate about pushing the boundaries of what is possible in robotics and who thrive in an environment defined by rapid iteration and deep technical rigor. ##### Tip Tycho.AI emphasizes a collaborative, interdisciplinary approach. Be prepared to discuss how your software solutions integrate with hardware constraints and real-world environmental variables.
Initial Screening
reportedGauge your technical background and alignment with Tycho.AI's mission.
What to demonstrate
- Gauge your technical background and alignment with Tycho.AI's mission
- Depth in Autonomy Systems
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.
Deep-Dive Technical Rounds
reportedIncludes coding assessments, system design discussions, and scenario-based interviews.
What to demonstrate
- Includes coding assessments, system design discussions, and scenario-based interviews
- Depth in Autonomy Systems
How to prepare
- Answer aloud and timed: What are the common failure modes in deep learning-based Perception models, and how do you mitigate them?
- Answer aloud and timed: Describe your experience with C++ optimization in resource-constrained environments.
PracHub editorial advice for the preparation topics above.
Think out loud
Our interviewers are as interested in your problem-solving journey as they are in the final answer. Explain your assumptions and the trade-offs you are considering.
Be ready for follow-ups
If you propose a solution, expect to be asked about its limitations or how it would perform under different conditions.
Know your resume
Be prepared to discuss any project on your resume in extreme detail, including the specific technical challenges you faced and how you overcame them.
Do not gloss over the "why" behind your design choices
If you choose a specific algorithm or data structure, be prepared to justify it against alternatives.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Explain the trade-offs between different path-planning algorithms for dynamic obstacle avoidance.
Explain the trade-offs between different path-planning algorithms for dynamic obstacle avoidance.
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?
How do you design modular interfaces to allow for rapid testing of new algorithms?
How do you design modular interfaces to allow for rapid testing of new algorithms?
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?
Canonicalise a request body into a stable idempotency fingerprint
idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.
Approach
- Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
- Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
- Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
- Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.
Follow-up
- A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
- Where does the fingerprint get computed relative to request decompression and the body-size limit?
Stop tag and share joins from fanning out a page
resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.
Approach
- Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
- Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
- Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
- Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.
Follow-up
- The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
- A resource can be shared with the same user twice under different permissions. Does your count change, and should it?
Write the update path that detects a concurrent edit
resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.
Approach
- One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
- Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
- State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
- Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.
Follow-up
- A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
- Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?
What are the common failure modes in deep learning-based Perception models, and how do you mitigate them?
What are the common failure modes in deep learning-based Perception models, and how do you mitigate them?
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 architect a distributed system for processing multi-modal sensor data?
How would you architect a distributed system for processing multi-modal sensor data?
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?
Explain how you ensure low-latency performance in a complex control loop.
Explain how you ensure low-latency performance in a complex control loop.
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?
Exports duplicate a row range about once a week
Roughly once a week an export writes a file containing a duplicated range of rows. The affected job_run rows show attempt = 1, status = succeeded, one started_at, and a lease_owner naming a different host from the one whose logs show the job starting. Leases last 30 seconds and are heartbeated every 10 from inside the handler; lease_expires_at is computed on the worker and compared against the database's now(). Find the mechanism, and give a fix that holds even if you cannot fix the clocks.
Approach
- Start from the fact that eliminates the obvious answer. attempt = 1 means no retry was recorded, so this is not a re-run after failure; two workers ran the same row concurrently and the takeover path never touched the counter. lease_owner naming a host other than the one that started the job is the same statement from the other side.
- Enumerate the mechanisms that cause a premature takeover, then find the signal that separates them. Either the lease genuinely expired because the heartbeat did not fire, which is what happens when the heartbeat runs on the handler's own thread and the handler makes a long blocking call, or it only appeared expired because two clocks disagree, since lease_expires_at is written from the worker's clock and evaluated against the database's. The discriminator is the distribution: incidents clustered on the longest exports indict the heartbeat, incidents clustered on one host indict skew. Measure both, and measure each host's offset against the database directly.
- Read the reclaim query precisely. In PostgreSQL now() is transaction start time, not statement time, so a reclaimer holding a long transaction compares against an older timestamp than expected; clock_timestamp() is the statement-time function. This is worth ruling in or out before you redesign anything, because it changes which rows look expired.
- Remove the second clock rather than trying to synchronise it. Issue and extend the lease in the database, with lease_expires_at = now() + interval '30 seconds' in both the claim and the heartbeat, so exactly one clock is ever compared and worker skew stops mattering to this predicate.
Follow-up
- The displaced worker has already streamed half the file to object storage. What makes that side effect safe to repeat?
- You now count takeovers. What alert fires on that counter, and at what threshold?
Built from the rounds and topics Tycho.AI candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Tycho.AI loop
- Write out the reported sequence: Initial Screening, Deep-Dive Technical Rounds.
- 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 2 reported rounds, with the weakest marked.
02Work Autonomy Systems
- Spend the session on Autonomy Systems, which Tycho.AI candidates report being tested on.
- Write one worked example in Autonomy Systems and time yourself on it.
Deliverable: One timed worked example in Autonomy Systems.
03Work Perception for Autonomous Systems
- Spend the session on Perception for Autonomous Systems, which Tycho.AI candidates report being tested on.
- Write one worked example in Perception for Autonomous Systems and time yourself on it.
Deliverable: One timed worked example in Perception for Autonomous Systems.
04Work State Estimation
- Spend the session on State Estimation, which Tycho.AI candidates report being tested on.
- Write one worked example in State Estimation and time yourself on it.
Deliverable: One timed worked example in State Estimation.
05Answer out loud: Technical and Domain Expertise
- Answer aloud, timed: How do you handle sensor noise in a real-time State Estimation pipeline?
- Answer aloud, timed: Explain the trade-offs between different path-planning algorithms for dynamic obstacle avoidance.
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 architect a distributed system for processing multi-modal sensor data?
- Answer aloud, timed: Explain how you ensure low-latency performance in a complex control loop.
Deliverable: Spoken answers to 2 reported System Design and Architecture question(s), under time.
07Answer out loud: Behavioral and Problem Solving
- Answer aloud, timed: Describe a time you encountered a critical system bug in production; how did you isolate and resolve it?
- Answer aloud, timed: How do you balance the need for rapid feature development with the requirement for long-term code maintainability?
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 sensor noise in a real-time State Estimation pipeline?
How do you handle sensor noise in a real-time State Estimation pipeline?
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 your experience with C++ optimization in resource-constrained environments.
Describe your experience with C++ optimization in resource-constrained environments.
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 time you encountered a critical system bug in production; how did you isolate and resolve it?
Describe a time you encountered a critical system bug in production; how did you isolate and resolve it?
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 balance the need for rapid feature development with the requirement for long-term code maintainabil
How do you balance the need for rapid feature development with the requirement for long-term code maintainability?
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 pivot your technical approach due to unexpected hardware limitations.
Tell me about a time you had to pivot your technical approach due to unexpected hardware limitations.
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 sensor noise in a real-time State Estimation pipeline?
- 02
Describe your experience with C++ optimization in resource-constrained environments.
- 03
Describe a time you encountered a critical system bug in production; how did you isolate and resolve it?
- 04
How do you balance the need for rapid feature development with the requirement for long-term code maintainability?
How long should I spend preparing for the interview?
Most successful candidates spend several weeks reviewing their core technical domain and practicing system design. Because our interviews are highly technical, prioritize deep dives into your areas of expertise rather than broad memorization.
Tycho.AI Software Engineer candidate reports ↗What differentiates top-tier candidates?
Successful candidates often demonstrate a "first principles" approach, showing that they understand the underlying math and physics of their work. They also excel at communicating their thought process clearly while under pressure.
Tycho.AI Software Engineer candidate reports ↗What is the company culture like?
Tycho.AI is a high-energy, mission-driven environment. We value intellectual honesty, rapid iteration, and a collaborative approach to solving hard problems.
Tycho.AI Software Engineer candidate reports ↗Are there remote work options?
Our engineering teams are primarily based in Cambridge, MA, as the nature of our work often requires close collaboration with hardware and physical testing environments.
Tycho.AI Software Engineer candidate reports ↗What topics does Tycho.AI test in interviews?
Tycho.AI interviews most often cover State Estimation, Autonomy Systems, Motion Planning, Controls Engineering, and Sensor Fusion. The exact emphasis depends on the specific role you apply for.
Tycho.AI 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.
- 01Tycho.AI 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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