Rula Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at Rula, you play a foundational role in building and scaling technology that transforms behavioral health delivery. Your work directly touches core systems such as patient onboarding, provider services, trust and safety, and patient matching. By developing reliable, high-performing software, you help bridge the gap between patients seeking care and providers ready to deliver it, making mental healthcare more accessible and efficient. This position demands both technical excellence and a deep appreciation for complex product problem spaces. You will collaborate closely with product managers, clinical operations, and adjacent engineering teams to architect features that balance rapid business growth with strict reliability standards.

This guide is scoped to a Software Engineer candidate at Rula.

Focus areas

• System Design
• Coding Interviews
• Game of Life (Conway’s) Coding Implementation

The Rula interview process

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

Recruiter Conversation

Initial discussion focused on background and motivation.

What to demonstrate

• Initial discussion focused on background and motivation
• Depth in System Design

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.

Hiring Manager Screen

In-depth evaluation of experience and cultural alignment.

What to demonstrate

• In-depth evaluation of experience and cultural alignment
• Depth in System Design

How to prepare

• Prepare two projects you led end to end, each with the decision you owned and what it cost.
• Have three questions about the team's roadmap and how success is measured in the first six months.

Technical Loop

Multi-stage technical assessment including live coding and system design.

What to demonstrate

• Multi-stage technical assessment including live coding and system design
• Depth in System Design

How to prepare

• Answer aloud and timed: Explain how you would write automated tests for a distributed messaging pipeline.
• Answer aloud and timed: Design a scalable messaging queue system for handling real-time notifications between patients and providers.

Live Coding Sessions

Candidates demonstrate coding skills in real-time.

What to demonstrate

• Candidates demonstrate coding skills in real-time
• Depth in System Design

How to prepare

• Answer aloud and timed: How would you architect a secure patient onboarding workflow that ensures high availability and compliance?
• Answer aloud and timed: Design a distributed rate limiter for API endpoints serving third-party telehealth integrations.

System Design Evaluation

Assessment of system architecture and design capabilities.

What to demonstrate

• Assessment of system architecture and design capabilities
• Depth in System Design

How to prepare

• Answer aloud and timed: Walk through how you would set up data partitioning for a rapidly growing user database.
• Answer aloud and timed: How do you approach monitoring and alerting for a distributed microservices architecture?

Deep Dives into Projects

Discussion of past projects to evaluate experience and problem-solving.

What to demonstrate

• Discussion of past projects to evaluate experience and problem-solving
• Depth in System Design

How to prepare

• Answer aloud and timed: Tell me about a time when you had to make a significant architectural tradeoff under tight deadlines.
• Answer aloud and timed: What has guided your career decisions so far, and why are you interested in Rula?

Where candidates lose points

Provide your own environment

Be prepared to use your own local IDE and whiteboarding software during technical rounds, as interviewers expect you to drive your own workspace setup.

Be exceptionally thorough in system design

Leave no stone unturned when walking through architecture diagrams; anticipate failure modes, scaling bottlenecks, and data consistency challenges proactively.

Communicate your thought process

Never code or design in silence. Continuously verbalize your assumptions, trade-offs, and alternative approaches so interviewers understand your reasoning.

Connect with the mission

Understand Rula's business model in behavioral health and be ready to articulate why you are genuinely motivated to work in telehealth and patient care access.

Questions & practice

Implement Conway's Game of Life from scratch in your own preferred IDE.

Medium · Technical / Coding

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?

Write a clean, modular program to process and transform incoming patient record payloads.

Medium · Technical / Coding

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?

How would you handle concurrency issues when multiple providers attempt to claim the same appointment slot?

Medium · Technical / Coding

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?

Write an algorithm to find optimal matches between patient intake criteria and available provider specialties.

Medium · Technical / Coding

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?

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?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

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

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

Explain how you would write automated tests for a distributed messaging pipeline.

Medium · Technical / Coding

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 scalable messaging queue system for handling real-time notifications between patients and providers.

Medium · System Design / 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?

How would you architect a secure patient onboarding workflow that ensures high availability and compliance?

Medium · System Design / 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?

Design a distributed rate limiter for API endpoints serving third-party telehealth integrations.

Medium · System Design / Architecture

Approach

1. Say who the caller is and what they do when the call fails halfway.
2. Define the identity of a request so a retry cannot double-apply it.
3. Separate accepted, pending, failed and confirmed; they are different facts.
4. Design the error taxonomy before the success shape; callers branch on it.

Follow-up

• What happens if the caller retries after a timeout?
• How does a client discover it is on an old version of this contract?

Walk through how you would set up data partitioning for a rapidly growing user database.

Medium · System Design / 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?

How do you approach monitoring and alerting for a distributed microservices architecture?

Medium · System Design / 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?

Edge instances grow 400 MB per hour until the nightly restart

Hard · memory leak · heap analysis · metric cardinality

Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.

Approach

1. Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
2. Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
3. Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
4. The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.

Follow-up

• Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
• How would you have detected this before an OOM, given the nightly restart masked the trend?

Suggested practice checklist

Built from the rounds and topics Rula candidates report.

Day 1 — Map the Rula loop

• Write out the reported sequence: Recruiter Conversation, Hiring Manager Screen, Technical Loop, Live Coding Sessions, System Design Evaluation, Deep Dives into Projects.
• 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 6 reported rounds, with the weakest marked.

Day 2 — Work System Design

• Spend the session on System Design, which Rula candidates report being tested on.
• Write one worked example in System Design and time yourself on it.

Deliverable: One timed worked example in System Design.

Day 3 — Work Coding Interviews

• Spend the session on Coding Interviews, which Rula candidates report being tested on.
• Write one worked example in Coding Interviews and time yourself on it.

Deliverable: One timed worked example in Coding Interviews.

Day 4 — Work Game of Life (Conway’s) Coding Implementation

• Spend the session on Game of Life (Conway’s) Coding Implementation, which Rula candidates report being tested on.
• Write one worked example in Game of Life (Conway’s) Coding Implementation and time yourself on it.

Deliverable: One timed worked example in Game of Life (Conway’s) Coding Implementation.

Day 5 — Answer out loud: Technical / Coding

• Answer aloud, timed: Implement Conway's Game of Life from scratch in your own preferred IDE.
• Answer aloud, timed: Write a clean, modular program to process and transform incoming patient record payloads.

Deliverable: Spoken answers to 2 reported Technical / Coding question(s), under time.

Day 6 — Answer out loud: System Design / Architecture

• Answer aloud, timed: Design a scalable messaging queue system for handling real-time notifications between patients and providers.
• Answer aloud, timed: How would you architect a secure patient onboarding workflow that ensures high availability and compliance?

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

Day 7 — Answer out loud: Behavioral & Culture Fit

• Answer aloud, timed: Tell me about a time when you had to make a significant architectural tradeoff under tight deadlines.
• Answer aloud, timed: What has guided your career decisions so far, and why are you interested in Rula?

Deliverable: Spoken answers to 2 reported Behavioral & Culture Fit question(s), under time.

Behavioural preparation

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

Tell me about a time when you had to make a significant architectural tradeoff under tight deadlines.

Medium · Behavioral & Culture Fit

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?

What has guided your career decisions so far, and why are you interested in Rula?

Medium · Behavioral & Culture Fit

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 time when you disagreed with a product requirement or technical direction and how you resolved

Medium · Behavioral & Culture Fit

Tell me about a time when you disagreed with a product requirement or technical direction and how you resolved it.

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?

Describe a complex project you owned from inception to delivery and the biggest obstacle you overcame.

Medium · Behavioral & Culture Fit

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?

How do you align with our mission of expanding access to mental healthcare?

Medium · Behavioral & Culture Fit

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?