Osmind Software Engineer Interview Guide

The 60-second brief

A Software Engineer at Osmind plays a critical role in building and scaling the foundational technology that powers the future of mental health medicine. Osmind develops software for treatment-resistant mental health practices, providing clinicians with specialized electronic health records (EHR) and clinical tools, while giving researchers access to aggregated, real-world data to advance new treatments. As an engineer here, your work directly impacts patient outcomes, clinical efficiency, and medical research. The engineering team at Osmind is tasked with solving complex problems around data modeling, system reliability, and intuitive user experiences. Because the platform handles sensitive healthcare data, engineers must balance high security and compliance standards with rapid, product-driven iteration.

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

Focus areas

• System design
• Coding interviews (live coding)
• Data structures

The Osmind interview process

Osmind candidates report 4 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screen

Initial discussion to align on your background and high-level technical interests.

What to demonstrate

• Initial discussion to align on your background and high-level technical interests
• 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 Chat

Conversational chat with the Head of Engineering to discuss the company's vision and assess cultural alignment.

What to demonstrate

• Conversational chat with the Head of Engineering to discuss the company's vision and assess 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 Screens

Focus on live coding challenges to evaluate technical skills.

What to demonstrate

• Focus on live coding challenges to evaluate technical skills
• Depth in System design

How to prepare

• Answer aloud and timed: Design a schema for a clinical scheduling system that supports recurring appointments and multi-provider availability.
• Answer aloud and timed: How would you architect a real-time notification system to alert clinicians when critical patient data is updated?

Virtual Onsite

Deep dive into system design, collaborative refactoring, and cross-functional alignment.

What to demonstrate

• Deep dive into system design, collaborative refactoring, and cross-functional alignment
• Depth in System design

How to prepare

• Answer aloud and timed: Design a secure, HIPAA-compliant system for exporting large datasets of aggregated clinical research data.
• Answer aloud and timed: Explain how you would handle database migrations for a high-traffic production database with zero downtime.

Where candidates lose points

Understand the Mission

Be ready to clearly articulate why you want to join Osmind and how your skills can help advance the company's goal of improving mental health treatments.

Practice Refactoring

Spend time reviewing open-source codebases or your own past projects. Practice identifying code smells and modularizing complex logic, as this is a key component of the onsite evaluation.

Do not underestimate the initial recruiter screen

Be prepared to answer basic questions about data modeling and your experience with their tech stack right from the first call.

Communicate Your Trade-offs

During the system design and coding rounds, always explain the trade-offs of your approach. Interviewers want to hear how you weigh factors like readability, performance, and development speed.

Questions & practice

Implement a function to merge and clean overlapping time intervals for patient scheduling.

Medium · Coding & Data Structures

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 a raw dataset of patient records, write a utility to parse and transform the data into a structured form

Medium · Coding & Data Structures

Given a raw dataset of patient records, write a utility to parse and transform the data into a structured format using maps and arrays.

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 optimize a search algorithm that queries a deeply nested JSON structure representing medical his

Medium · Coding & Data Structures

How would you optimize a search algorithm that queries a deeply nested JSON structure representing medical histories?

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?

Hold a per-tenant active cap against concurrent creates

Hard · write skew · isolation levels · constraints

A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.

Approach

1. Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
2. Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
3. Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
4. State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.

Follow-up

• A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
• The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?

Replace offset paging on the resource feed with keyset

Easy · keyset pagination · composite index · sort order

resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.

Approach

1. Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
2. Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
3. Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
4. Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.

Follow-up

• The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
• Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?

Design a schema for a clinical scheduling system that supports recurring appointments and multi-provider avail

Medium · System Design & Data Modeling

Design a schema for a clinical scheduling system that supports recurring appointments and multi-provider availability.

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 real-time notification system to alert clinicians when critical patient data is upda

Medium · System Design & Data Modeling

How would you architect a real-time notification system to alert clinicians when critical patient data is updated?

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 secure, HIPAA-compliant system for exporting large datasets of aggregated clinical research data.

Medium · System Design & Data Modeling

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 handle database migrations for a high-traffic production database with zero downtime.

Medium · System Design & Data Modeling

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?

Listing latency scales with page size, not with filters

Easy · n+1 · latency · query batching

The tenant listing endpoint reads resource filtered by tenant_id and status, ordered by updated_at DESC, and returns each row plus the owner's display name from app_user and the actor of that resource's latest resource_revision. p99 is 55 ms at 10 rows per page and 1.4 s at 200. Database telemetry shows 401 statements per request, each under 1 ms, and nothing in the slow-query log. Diagnose the cause and give the fix, stating the statement count per request and the p99 you expect afterwards.

Approach

1. Read the counters before forming a theory. 401 statements for 200 rows is one driver query plus two per row, and sub-millisecond execution with an empty slow-query log rules out a bad plan. The time is round trips, which is why it is invisible in every per-query metric and scales with rows returned rather than with filter selectivity.
2. Name the two per-row statements from their normalised text: a single-row app_user lookup by user_id, and a resource_revision lookup by resource_id ordered by version DESC LIMIT 1. Confirm by dropping those two response fields and watching the statement count fall to one. That locates the calls in the serialisation layer, not the repository.
3. Check that the arithmetic accounts for the whole gap. Measure one round trip to the replica in isolation; 400 trips at roughly 3 ms of network plus 0.2 ms of execution is about 1.3 s on top of a 55 ms baseline, which matches. If the multiplication had fallen short, the N+1 would only be part of the story and you would keep looking.
4. Batch both lookups. Collect owner_user_ids and resource_ids from the driver query, then issue WHERE tenant_id = $1 AND user_id = ANY($2) for the users, and PostgreSQL's SELECT DISTINCT ON (resource_id) ... WHERE resource_id = ANY($2) ORDER BY resource_id, version DESC for the latest revision, which the UNIQUE (resource_id, version) index serves directly. On an engine without DISTINCT ON, use a lateral join or a row_number window. Three statements per request at any page size.

Follow-up

• The page size is capped at 200 today. What breaks first if it is raised to 2,000, and is it still this bug?
• How do you stop the next N+1 from reaching production, given that no individual query is slow and the endpoint's tests pass?

Suggested practice checklist

Built from the rounds and topics Osmind candidates report.

Day 1 — Map the Osmind loop

• Write out the reported sequence: Recruiter Screen, Hiring Manager Chat, Technical Screens, Virtual Onsite.
• 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 4 reported rounds, with the weakest marked.

Day 2 — Work System design

• Spend the session on System design, which Osmind 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 (live coding)

• Spend the session on Coding interviews (live coding), which Osmind candidates report being tested on.
• Write one worked example in Coding interviews (live coding) and time yourself on it.

Deliverable: One timed worked example in Coding interviews (live coding).

Day 4 — Work Data structures

• Spend the session on Data structures, which Osmind candidates report being tested on.
• Write one worked example in Data structures and time yourself on it.

Deliverable: One timed worked example in Data structures.

Day 5 — Answer out loud: Coding & Data Structures

• Answer aloud, timed: Implement a function to merge and clean overlapping time intervals for patient scheduling.
• Answer aloud, timed: Given a raw dataset of patient records, write a utility to parse and transform the data into a structured format using maps and arrays.

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

Day 6 — Answer out loud: System Design & Data Modeling

• Answer aloud, timed: Design a schema for a clinical scheduling system that supports recurring appointments and multi-provider availability.
• Answer aloud, timed: How would you architect a real-time notification system to alert clinicians when critical patient data is updated?

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

Day 7 — Answer out loud: Behavioral & Mission Alignment

• Answer aloud, timed: Why do you want to work at Osmind, and what excites you about the mental health technology space?
• Answer aloud, timed: Describe a time when you had to refactor a complex piece of legacy code. How did you approach it, and what was the outcome?

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

Behavioural preparation

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

Write a function to detect duplicate entries in a database migration log and resolve conflicts based on timest

Medium · Coding & Data Structures

Write a function to detect duplicate entries in a database migration log and resolve conflicts based on timestamps.

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 do you want to work at Osmind, and what excites you about the mental health technology space?

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?

Describe a time when you had to refactor a complex piece of legacy code. How did you approach it, and what was

Medium · Behavioral & Mission Alignment

Describe a time when you had to refactor a complex piece of legacy code. How did you approach it, and what was the outcome?

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 handle a situation where a product manager's requirements conflict with technical best practices or

Medium · Behavioral & Mission Alignment

How do you handle a situation where a product manager's requirements conflict with technical best practices or timelines?

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 challenging technical bug you resolved in production. What was your process for diagnosing and

Medium · Behavioral & Mission Alignment

Tell me about a challenging technical bug you resolved in production. What was your process for diagnosing and fixing 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?