As a Software Engineer at AeroVect, you step into a high-impact role driving the autonomous transformation of ground handling operations for global aviation. You will build and scale core components of advanced autonomy stacks—spanning perception, motion planning, localization, and platform infrastructure—that enable vehicles to navigate complex airport environments safely and efficiently. This position places you at the intersection of rigorous robotics engineering and real-world commercial deployment, working alongside top-tier talent backed by leading venture investors. Your day-to-day contributions directly influence production systems utilized by the world's largest airlines and ground service providers. Whether you are architecting 3D object detection models, refining multi-modal sensor fusion pipelines, or optimizing platform stability, your code directly controls heavy machinery in demanding operational domains. The problem space requires balancing theoretical state-of-the-art robotics algorithms with the strict reliability, safety, and latency requirements of physical hardware in dynamic environments. Expect a fast-paced, intellectually demanding engineering culture where ownership and autonomy are expected from day one. You will tackle complex technical challenges involving sparse sensor data, edge cases in object tracking, and scalable infrastructure management.
Initial Screening Interview
reportedThe process begins with a screening interview to assess the candidate's background and fit for the role.
What to demonstrate
- The process begins with a screening interview to assess the candidate's background and fit for the role
- Depth in 3D Object Detection
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 Coding Assessment
reportedCandidates complete a technical coding assessment to evaluate their programming skills.
What to demonstrate
- Candidates complete a technical coding assessment to evaluate their programming skills
- Depth in 3D Object Detection
How to prepare
- Answer aloud and timed: What are the primary failure modes in visual localization, and how do you mitigate sensor drift in GPS-denied environments?
- Answer aloud and timed: Can you explain the trade-offs between different motion planning paradigms in dynamic, unstructured environments?
Onsite Interview
reportedSuccessful candidates participate in an onsite interview, which includes debugging tasks and discussions about company values.
What to demonstrate
- Successful candidates participate in an onsite interview, which includes debugging tasks and discussions about company values
- Depth in 3D Object Detection
How to prepare
- Answer aloud and timed: How do you design and evaluate metrics pipelines for perception models in production?
- Answer aloud and timed: Evaluate your fluency in core languages, object-oriented principles, and algorithm efficiency.
PracHub editorial advice for the preparation topics above.
Going into the loop without having done this.
Emphasize production readiness: When discussing past projects, always highlight how you addressed reliability, edge cases, safety, and performance constraints, not just how the algorithm worked in simulation.
Going into the loop without having done this.
Communicate your thought process: Interviewers value clarity and structured thinking. Talk through your assumptions, state your constraints, and explain why you are choosing a specific data structure or architectural pattern.
Going into the loop without having done this.
Master your resume projects: Be ready to dive deep into any technical project listed on your CV, explaining your specific contributions, architectural hurdles, and the ultimate business or technical impact.
Going into the loop without having done this.
Prepare questions about the domain: Ask insightful questions regarding sensor suites, fleet data pipelines, and safety validation protocols to demonstrate your genuine engagement with ground handling autonomy.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Evaluate your fluency in core languages, object-oriented principles, and algorithm efficiency.
Evaluate your fluency in core languages, object-oriented principles, and algorithm efficiency.
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?
Walk me through your implementation of a core object-oriented module in C++ for handling spatial data.
Walk me through your implementation of a core object-oriented module in C++ for handling spatial data.
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?
Write a function to filter and track dynamic objects from a stream of point cloud inputs.
Write a function to filter and track dynamic objects from a stream of point cloud inputs.
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 profile and optimize memory allocation bottlenecks in real-time robotics software?
How do you profile and optimize memory allocation bottlenecks in real-time robotics software?
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
Can you explain how you handle concurrency and thread safety in high-frequency control loops?
Can you explain how you handle concurrency and thread safety in high-frequency control loops?
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
Explain why the owner filter ignores the listing index
The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.
Approach
- Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
- For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
- Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
- Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.
Follow-up
- 90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
- A dashboard runs this for 40 owners in one page load. What changes about the design?
Find version gaps and relay lag with window functions
outbox_event holds event_id, aggregate_type, aggregate_id, aggregate_version, event_type, payload, status ('pending','published','dead'), attempts, created_at, published_at. A projection is missing rows and you must decide whether the relay skipped events or the consumer dropped them. Write three queries over the last seven days: one listing every aggregate_id whose published aggregate_version sequence has a hole, one giving per-day counts with a running total, and one returning the newest published event per aggregate. For each, say where the window function is evaluated relative to WHERE and LIMIT. PostgreSQL 16.
Approach
- Gaps: compute lead(aggregate_version) OVER (PARTITION BY aggregate_id ORDER BY aggregate_version) in a subquery, then filter next_version <> aggregate_version + 1 in the outer query. Window functions are evaluated after WHERE, GROUP BY and HAVING and before the outer ORDER BY and LIMIT, so the predicate cannot sit in the same WHERE clause and PostgreSQL 16 has no QUALIFY.
- Say what the seven-day filter does to the answer: it truncates every partition, so the first row per aggregate has no predecessor inside the window and a hole spanning the boundary is invisible. Widen the window, or join to resource.version as the authority for the true maximum.
- Running total: SELECT date_trunc('day', created_at) AS d, count() AS n, sum(count()) OVER (ORDER BY date_trunc('day', created_at) ROWS UNBOUNDED PRECEDING). An aggregate inside a window call is legal because grouping runs before windowing. The grouping key is unique per row here so ROWS and RANGE agree, but write the frame anyway — over ungrouped rows with tied timestamps the default RANGE frame pulls in every peer row and the total jumps.
- Newest per aggregate: DISTINCT ON (aggregate_id) ... ORDER BY aggregate_id, aggregate_version DESC is the cheap PostgreSQL-only form when an index matches that order; row_number() OVER (PARTITION BY aggregate_id ORDER BY aggregate_version DESC) = 1 is the portable form and needs a subquery for the same evaluation-order reason as the gap query.
Follow-up
- Relay failover redelivers events. Does a duplicate break the gap query, and how would you detect one from this table alone?
- Turn the gap check into a continuous monitor rather than a query someone runs after an incident. What does it watch?
Test your grasp of robotics, autonomy architectures, and sensor integration principles.
Test your grasp of robotics, autonomy architectures, and sensor integration principles.
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 approach multi-modal sensor fusion using camera, LiDAR, and radar data for 3D object detection?
How do you approach multi-modal sensor fusion using camera, LiDAR, and radar data for 3D object detection?
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?
What are the primary failure modes in visual localization, and how do you mitigate sensor drift in GPS-denied
What are the primary failure modes in visual localization, and how do you mitigate sensor drift in GPS-denied environments?
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 explain the trade-offs between different motion planning paradigms in dynamic, unstructured environmen
Can you explain the trade-offs between different motion planning paradigms in dynamic, unstructured environments?
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 design and evaluate metrics pipelines for perception models in production?
How do you design and evaluate metrics pipelines for perception models in production?
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?
Assess your ability to scale systems, manage infrastructure, and structure complex software.
Assess your ability to scale systems, manage infrastructure, and structure complex software.
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 would you design a data collection, logging, and evaluation pipeline for training autonomous driving model
How would you design a data collection, logging, and evaluation pipeline for training autonomous driving models at scale?
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 distributed platform infrastructure capable of ingesting telemetry data from thousands of deployed ve
Design a distributed platform infrastructure capable of ingesting telemetry data from thousands of deployed vehicles.
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 architect a fault-tolerant software stack that gracefully handles hardware component failures?
How do you architect a fault-tolerant software stack that gracefully handles hardware component failures?
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?
p99 jumped on one listing filter while p50 stayed flat
After a release that added an owner_user_id filter to the resource listing, p99 rose from 90 ms to 1.9 s while p50 stayed at 40 ms. Traffic and row counts are unchanged. resource carries the index (tenant_id, status, updated_at DESC, resource_id DESC). The new query filters tenant_id and owner_user_id, orders by updated_at DESC, resource_id DESC, and takes 20 rows. On PostgreSQL, explain the shape of the regression, prove it from a query plan, and give the index you would add.
Approach
- Start from the shape. A flat p50 with a moved p99 means a subset of requests changed cost, not all of them, so the first job is naming the subset. Bucket the endpoint's latency by the tenant's row count; the natural hypothesis is that large tenants are a small share of requests and all of the tail.
- Get the plan for the new query on a large tenant with EXPLAIN (ANALYZE, BUFFERS). Expect an index scan over the tenant's range, a filter discarding most of it, then a Sort feeding the Limit, possibly reporting Sort Method: external merge Disk. Read actual rows on the scan node, not estimated.
- Explain why the existing index cannot serve it. A composite B-tree is seekable only as a left prefix, and with no equality predicate on status the scan cannot treat updated_at as an ordering, because rows in the tenant's range are ordered by status first. Everything matching must be read and sorted before LIMIT 20 can apply, so a tenant with 400,000 rows pays 400,000 rows to return 20.
- Add (tenant_id, owner_user_id, updated_at DESC, resource_id DESC). Equality on the first two columns leaves the index ordered by updated_at within that pair, so the plan becomes an index scan that stops after 20 rows with no Sort node. PostgreSQL can scan a B-tree backwards, so the DESC markers matter only if the two sort columns ever disagree in direction; keeping them explicit documents the order the keyset cursor depends on.
Follow-up
- The endpoint paginates with OFFSET. What does page 500 cost with your index, and what does the keyset version cost?
- How would you have caught this before release, given that a 10,000-row seed database produces the same plan shape at an unnoticeable cost?
Built from the rounds and topics AeroVect candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the AeroVect loop
- Write out the reported sequence: Initial Screening Interview, Technical Coding Assessment, Onsite Interview.
- 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 3D Object Detection
- Spend the session on 3D Object Detection, which AeroVect candidates report being tested on.
- Write one worked example in 3D Object Detection and time yourself on it.
Deliverable: One timed worked example in 3D Object Detection.
03Work Multi-Modal Perception
- Spend the session on Multi-Modal Perception, which AeroVect candidates report being tested on.
- Write one worked example in Multi-Modal Perception and time yourself on it.
Deliverable: One timed worked example in Multi-Modal Perception.
04Work Python
- Spend the session on Python, which AeroVect candidates report being tested on.
- Write one worked example in Python and time yourself on it.
Deliverable: One timed worked example in Python.
05Answer out loud: Technical and Domain Knowledge
- Answer aloud, timed: Test your grasp of robotics, autonomy architectures, and sensor integration principles.
- Answer aloud, timed: How do you approach multi-modal sensor fusion using camera, LiDAR, and radar data for 3D object detection?
Deliverable: Spoken answers to 2 reported Technical and Domain Knowledge question(s), under time.
06Answer out loud: Coding and Implementation
- Answer aloud, timed: Evaluate your fluency in core languages, object-oriented principles, and algorithm efficiency.
- Answer aloud, timed: Walk me through your implementation of a core object-oriented module in C++ for handling spatial data.
Deliverable: Spoken answers to 2 reported Coding and Implementation question(s), under time.
07Answer out loud: System Design and Architecture
- Answer aloud, timed: Assess your ability to scale systems, manage infrastructure, and structure complex software.
- Answer aloud, timed: How would you design a data collection, logging, and evaluation pipeline for training autonomous driving models at scale?
Deliverable: Spoken answers to 2 reported System Design and Architecture 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.
Understand your background, project ownership, collaboration style, and engineering values.
Understand your background, project ownership, collaboration style, and engineering values.
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?
Walk me through a challenging technical project on your resume where you had to lead a solution autonomously.
Walk me through a challenging technical project on your resume where you had to lead a solution autonomously.
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 when you disagreed with a teammate on an architectural decision; how did you resolve it?
Tell me about a time when you disagreed with a teammate on an architectural decision; how did you 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 handle shifting priorities and tight timelines in a fast-moving startup environment?
How do you handle shifting priorities and tight timelines in a fast-moving startup environment?
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
Understand your background, project ownership, collaboration style, and engineering values.
- 02
Walk me through a challenging technical project on your resume where you had to lead a solution autonomously.
- 03
Tell me about a time when you disagreed with a teammate on an architectural decision; how did you resolve it?
- 04
How do you handle shifting priorities and tight timelines in a fast-moving startup environment?
How difficult are the technical interviews at AeroVect?
The technical rounds are rigorous and practical, focusing heavily on domain-specific systems design and real-world C++ or Python implementation rather than puzzle-style algorithmic questions. Expect interviewers to probe deeply into your past architectural decisions and code quality.
AeroVect Software Engineer candidate reports ↗How much preparation time should I plan for?
Most candidates benefit from 3 to 4 weeks of targeted preparation, focusing on refreshing modern C++ memory management, reviewing core autonomy algorithms, and practicing system design for distributed or real-time robotics systems.
AeroVect Software Engineer candidate reports ↗What differentiates successful candidates during the interview loop?
Successful candidates distinguish themselves by demonstrating deep intuition for physical systems, articulating clear architectural trade-offs, and showing an ability to work autonomously through ambiguous problem spaces.
AeroVect Software Engineer candidate reports ↗What is the typical hiring timeline from initial screen to offer?
While the process can sometimes experience scheduling pauses or variable response times, a standard interview pipeline typically moves from an initial recruiter screen to a technical round and onsite over the course of several weeks.
AeroVect Software Engineer candidate reports ↗Are the roles remote or on-site?
AeroVect offers a mix of remote positions, hybrid setups, and office-based roles in hubs like San Francisco, CA, and Atlanta, GA, depending on the specific engineering team and sub-specialization.
AeroVect Software Engineer candidate reports ↗How hard is the AeroVect interview?
Candidates most commonly rate AeroVect interviews as medium, based on 15 reported interviews.
AeroVect Software Engineer candidate reports ↗What topics does AeroVect test in interviews?
AeroVect interviews most often cover Autonomous Motion Planning, C++, Control Architecture, Systems Engineering, and Safety Engineering. The exact emphasis depends on the specific role you apply for.
AeroVect Software Engineer candidate reports ↗Where is AeroVect headquartered?
AeroVect is headquartered in South San Francisco, US.
AeroVect Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01AeroVect Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
candidate · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
platform · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
platform · Accessed 2026-09-22
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