& General Intuition Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at & General Intuition, you are responsible for building the core infrastructure, simulation software, and developer tools that empower the world's leading autonomy and robotics teams. The company operates at the intersection of high-performance computing, complex physical simulation, and modern web infrastructure. Engineers here build high-fidelity simulation environments, automated scenario generation tools, cloud-scale developer platforms, and mission-control user interfaces that enable autonomous vehicles, robotics systems, and defense platforms to operate safely in the real world. The impact of this role extends across the entire autonomous system ecosystem.

This guide is scoped to a Software Engineer candidate at & General Intuition.

Focus areas

• C++ programming
• Data Structures & Algorithms (DSA)
• Runtime efficiency & performance optimization

The & General Intuition interview process

& General Intuition candidates report 7 rounds over 4-6 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screen

Initial contact to align on background, track preference, and location expectations.

What to demonstrate

• Initial contact to align on background, track preference, and location expectations
• Depth in C++ programming

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

One or two technical interviews focused on data structures, algorithms, and coding in a shared editor.

What to demonstrate

• One or two technical interviews focused on data structures, algorithms, and coding in a shared editor
• Depth in C++ programming

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.

Onsite Interview Loop

A series of four to five rounds conducted virtually, including coding, system design, and behavioral interviews.

What to demonstrate

• A series of four to five rounds conducted virtually
• Including coding, system design, and behavioral interviews

How to prepare

• Answer aloud and timed: Given an array of task dependencies with execution times, schedule tasks across parallel threads to minimize overall processing latency.
• Answer aloud and timed: Implement a simplified 2D particle motion simulation engine and handle dynamic boundary interactions efficiently.

Advanced Coding Rounds

Interviews focused on advanced coding challenges.

What to demonstrate

• Interviews focused on advanced coding challenges
• Depth in C++ programming

How to prepare

• Answer aloud and timed: Discuss memory management strategies and cache locality optimizations when updating hundreds of thousands of simulation object states per frame in C++.
• Answer aloud and timed: How would you structure a spatial hashing mechanism to handle fast proximity checks in large-scale simulation environments?

System Design Interview

Comprehensive interview assessing system design or architecture skills.

What to demonstrate

• Comprehensive interview assessing system design or architecture skills
• Depth in C++ programming

How to prepare

• Answer aloud and timed: Design a dynamic state matrix update pipeline for vehicle trajectory prediction under fluctuating frame rates.
• Answer aloud and timed: Explain trade-offs between fixed-time-step and variable-time-step simulation loops when dealing with numerical instability.

Domain-Specific Deep Dive

Tailored interview focused on specific domain relevant to the target team.

What to demonstrate

• Tailored interview focused on specific domain relevant to the target team
• Depth in C++ programming

How to prepare

• Answer aloud and timed: Design a real-time telemetry streaming and logging service capable of processing gigabytes of autonomous vehicle simulation data per second.
• Answer aloud and timed: Architect a web-based Mission Control portal for visualizing complex 3D simulation replays with sub-second latencies.

Behavioral Round

Interview assessing past experiences and cultural alignment.

What to demonstrate

• Interview assessing past experiences and cultural alignment
• Depth in C++ programming

How to prepare

• Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
• Re-read the description of the behavioral round above and write down what you would ask to confirm before it.

Where candidates lose points

Optimize for Speed and Precision

Practice solving complex data structure problems while writing clean, working code rapidly. The team places high value on fast execution and minimal syntax or logic bugs.

Think Out Loud and Embrace Hints

Interviewers at & General Intuition are highly collaborative and act as pair programmers. Talk through your initial thoughts before coding, and actively integrate guidance or hints offered by your interviewer.

Comparing floating-point values for equality, or holding money in them

Binary floating point cannot represent 0.1 exactly, so repeated addition drifts and an equality check fails on values that are mathematically equal. Store currency as integer minor units or a decimal type, and compare floats against a tolerance you chose for a stated reason.

Questions & practice

Implement an efficient algorithm to detect path collisions between multi-agent systems in a 2D bounding grid.

Medium · Data Structures & Algorithmic Problem So

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. 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?

Traversed a dynamic graph representing street intersections to find all optimal path routes subject to dynamic

Medium · Data Structures & Algorithmic Problem So

Traversed a dynamic graph representing street intersections to find all optimal path routes subject to dynamic constraints.

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. 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?

Optimize a recursive dynamic programming solution to run within strict memory and execution time limits.

Medium · Data Structures & Algorithmic Problem So

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. 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?

Given an array of task dependencies with execution times, schedule tasks across parallel threads to minimize o

Medium · Data Structures & Algorithmic Problem So

Given an array of task dependencies with execution times, schedule tasks across parallel threads to minimize overall processing latency.

Approach

1. Say what the runtime actually does before reasoning about the code.
2. Name what is shared across threads and what owns each piece of state.
3. Identify the window where an invariant is briefly untrue.
4. 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?

Discuss memory management strategies and cache locality optimizations when updating hundreds of thousands of s

Medium · Domain-Specific & Simulation Engineering

Discuss memory management strategies and cache locality optimizations when updating hundreds of thousands of simulation object states per frame in C++.

Approach

1. Say what the runtime actually does before reasoning about the code.
2. Name what is shared across threads and what owns each piece of state.
3. Identify the window where an invariant is briefly untrue.
4. 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?

How do you approach debugging a silent, non-reproducible race condition in a multi-threaded system?

Medium · Behavioral & Mission Alignment

Approach

1. Say what the runtime actually does before reasoning about the code.
2. Name what is shared across threads and what owns each piece of state.
3. Identify the window where an invariant is briefly untrue.
4. 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

Medium · composite index · query plans · sort elimination

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

1. 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.
2. 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.
3. 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.
4. 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?

Stop tag and share joins from fanning out a page

Medium · join fan-out · semi-joins · many-to-many

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

1. 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.
2. 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.
3. 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.
4. 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?

Given a stream of simulation sensor data, design a data structure that supports fast range query lookups and i

Medium · Data Structures & Algorithmic Problem So

Given a stream of simulation sensor data, design a data structure that supports fast range query lookups and insertions in logarithmic time.

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. 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?

Implement a simplified 2D particle motion simulation engine and handle dynamic boundary interactions efficient

Medium · Domain-Specific & Simulation Engineering

Implement a simplified 2D particle motion simulation engine and handle dynamic boundary interactions efficiently.

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. 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 structure a spatial hashing mechanism to handle fast proximity checks in large-scale simulation

Medium · Domain-Specific & Simulation Engineering

How would you structure a spatial hashing mechanism to handle fast proximity checks in large-scale simulation environments?

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. Work from the requirement backwards to the design.

Follow-up

• What assumption would you test first?
• How would you know your answer was wrong?

Design a dynamic state matrix update pipeline for vehicle trajectory prediction under fluctuating frame rates.

Medium · Domain-Specific & Simulation Engineering

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. 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 trade-offs between fixed-time-step and variable-time-step simulation loops when dealing with numerical

Medium · Domain-Specific & Simulation Engineering

Explain trade-offs between fixed-time-step and variable-time-step simulation loops when dealing with numerical instability.

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. Work from the requirement backwards to the design.

Follow-up

• What assumption would you test first?
• How would you know your answer was wrong?

Design a real-time telemetry streaming and logging service capable of processing gigabytes of autonomous vehic

Medium · System Design & Software Architecture

Design a real-time telemetry streaming and logging service capable of processing gigabytes of autonomous vehicle simulation data per second.

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. 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?

Architect a web-based Mission Control portal for visualizing complex 3D simulation replays with sub-second lat

Medium · System Design & Software Architecture

Architect a web-based Mission Control portal for visualizing complex 3D simulation replays with sub-second latencies.

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. 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 modular plugin architecture that allows external autonomy stack clients to seamlessly hook into an in

Medium · System Design & Software Architecture

Design a modular plugin architecture that allows external autonomy stack clients to seamlessly hook into an internal simulation engine.

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. 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 distributed automated regression testing system for massive simulation test suites runn

Medium · System Design & Software Architecture

How would you design a distributed automated regression testing system for massive simulation test suites running across hundreds of cloud nodes?

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. 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 would decouple front-end visualization engines from back-end physical simulation loops to ensu

Medium · System Design & Software Architecture

Explain how you would decouple front-end visualization engines from back-end physical simulation loops to ensure responsive frame rates.

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. 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 me through a past engineering project where you had to quickly prototype an approach before settling on a

Medium · Behavioral & Mission Alignment

Walk me through a past engineering project where you had to quickly prototype an approach before settling on an optimal production architecture.

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. 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?

Every query on one table stalls for forty seconds mid-deploy

Medium · lock contention · ddl · migrations

During a release on PostgreSQL, every query touching resource times out for about 40 seconds and then recovers with no intervention. The release ran one migration, ALTER TABLE resource ADD COLUMN archived_reason TEXT, and the migration log shows it completing in 6 ms. Unrelated tables showed no change in error rate. Explain how a 6 ms statement caused a 40-second stall, give the ordered checks you would run on a live system to confirm it, and give the migration procedure that prevents a repeat.

Approach

1. Separate the statement's duration from the lock's duration. ADD COLUMN with no default is a catalogue-only change and genuinely runs in milliseconds, but it requires ACCESS EXCLUSIVE, and it cannot acquire that until every transaction already touching the table has finished.
2. Account for the queueing, which is the part that surprises people. A lock request that is waiting blocks later requests for conflicting modes behind it rather than letting them overtake, so one long-open transaction holds the DDL and the DDL holds all the traffic. The stall length is set by the longest open transaction, not by the size of the change.
3. Confirm on a live system in this order: pg_stat_activity for that table ordered by xact_start, looking for the oldest transaction and specifically for state = idle in transaction; then pg_locks where granted = false to find the waiter; then join them on pid to name blocker and blocked. pg_blocking_pids() does that join for you and is the fastest single call.
4. Prevent rather than merely time it better. Set lock_timeout to a second or two on the migration session so the DDL abandons the queue after a bounded wait and is retried, instead of holding it for as long as the oldest transaction lives. Be exact about what that buys: queries arriving during the wait still queue behind the pending ACCESS EXCLUSIVE request, so each attempt costs them up to one lock_timeout of added latency. The outage goes from 40 seconds to about one second per attempt, not to zero. Also run migrations away from deploy-time peaks, and put a statement timeout and an idle-in-transaction timeout on the analytics role that opens the long transactions.

Follow-up

• The same release also wants NOT NULL on that column. What is the sequence that gets there without a long lock?
• Your lock_timeout retry fails ten times in a row because the analytics transaction is always open. What do you change?

Suggested practice checklist

Built from the rounds and topics & General Intuition candidates report.

Day 1 — Map the & General Intuition loop

• Write out the reported sequence: Recruiter Screen, Technical Phone Screens, Onsite Interview Loop, Advanced Coding Rounds, System Design Interview, Domain-Specific Deep Dive.
• 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 7 reported rounds, with the weakest marked.

Day 2 — Work C++ programming

• Spend the session on C++ programming, which & General Intuition candidates report being tested on.
• Write one worked example in C++ programming and time yourself on it.

Deliverable: One timed worked example in C++ programming.

Day 3 — Work Data Structures & Algorithms (DSA)

• Spend the session on Data Structures & Algorithms (DSA), which & General Intuition candidates report being tested on.
• Write one worked example in Data Structures & Algorithms (DSA) and time yourself on it.

Deliverable: One timed worked example in Data Structures & Algorithms (DSA).

Day 4 — Work Runtime efficiency & performance optimization

• Spend the session on Runtime efficiency & performance optimization, which & General Intuition candidates report being tested on.
• Write one worked example in Runtime efficiency & performance optimization and time yourself on it.

Deliverable: One timed worked example in Runtime efficiency & performance optimization.

Day 5 — Answer out loud: Data Structures & Algorithmic Problem Solving

• Answer aloud, timed: Implement an efficient algorithm to detect path collisions between multi-agent systems in a 2D bounding grid.
• Answer aloud, timed: Given a stream of simulation sensor data, design a data structure that supports fast range query lookups and insertions in logarithmic time.

Deliverable: Spoken answers to 2 reported Data Structures & Algorithmic Problem Solving question(s), under time.

Day 6 — Answer out loud: Domain-Specific & Simulation Engineering

• Answer aloud, timed: Implement a simplified 2D particle motion simulation engine and handle dynamic boundary interactions efficiently.
• Answer aloud, timed: Discuss memory management strategies and cache locality optimizations when updating hundreds of thousands of simulation object states per frame in C++.

Deliverable: Spoken answers to 2 reported Domain-Specific & Simulation Engineering question(s), under time.

Day 7 — Answer out loud: System Design & Software Architecture

• Answer aloud, timed: Design a real-time telemetry streaming and logging service capable of processing gigabytes of autonomous vehicle simulation data per second.
• Answer aloud, timed: Architect a web-based Mission Control portal for visualizing complex 3D simulation replays with sub-second latencies.

Deliverable: Spoken answers to 2 reported System Design & Software Architecture question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

Describe a time when you had to make a complex architectural trade-off under strict deadline pressure.

Medium · Behavioral & Mission Alignment

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. 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?

Why are you specifically interested in solving simulation and safety infrastructure challenges for autonomous

Medium · Behavioral & Mission Alignment

Why are you specifically interested in solving simulation and safety infrastructure challenges for autonomous systems at & General Intuition?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. 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 situation where you received tough technical feedback during code review and how you adapted y

Medium · Behavioral & Mission Alignment

Tell me about a situation where you received tough technical feedback during code review and how you adapted your implementation.

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. 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?