As a Software Engineer at Talkiatry, you will build and scale the technology that makes high-quality, responsible psychiatric care accessible to tens of thousands of patients. Talkiatry sits at the intersection of clinical excellence and modern technology, meaning your work directly impacts patient intake, clinician workflows, and overall care delivery. Unlike traditional software roles, engineering here requires a deep appreciation for system reliability, data integrity, and compliance, as the platforms you build handle highly sensitive patient health information. The engineering team is tasked with solving complex operational and clinical challenges. This includes refining the patient matching algorithm, optimizing the clinical intake funnel, and ensuring seamless integrations with Electronic Health Record (EHR) systems. You will work on products that automate scheduling SLAs, streamline medication categorization, and improve the communication pipeline between patients and clinical operations. Your contributions will help transform what is often a fragmented healthcare experience into a structured, reliable, and empathetic journey. To succeed in this role, you must be a pragmatic problem-solver who values shipping functional software over theoretical perfection. The team operates in a fast-paced environment where priorities can shift, requiring engineers to be highly adaptable and collaborative.
Recruiter Screening
reportedInitial discussion about your background, career goals, and interest in Talkiatry.
What to demonstrate
- Initial discussion about your background, career goals, and interest in Talkiatry
- Depth in SQL
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.
SQL Assessment
reportedHands-on assessment to test your data-modeling and querying skills.
What to demonstrate
- Hands-on assessment to test your data-modeling and querying skills
- Depth in SQL
How to prepare
- Answer aloud and timed: How would you optimize a slow-performing SQL query that retrieves patient appointment histories for our clinical portal?
- Answer aloud and timed: Given a database of clinician schedules, write a query to find overlapping or double-booked time slots.
Technical Interviews
reportedMeet with senior engineers and engineering directors to evaluate system design and behavioral fit.
What to demonstrate
- Meet with senior engineers and engineering directors to evaluate system design and behavioral fit
- Depth in SQL
How to prepare
- Answer aloud and timed: Describe how you would design an automated system to categorize and validate patient medications during the intake process.
- Answer aloud and timed: How would you build a resilient notification service that alerts clinical operations when a critical patient SLA is about to be missed?
Final Technical Review
reportedFocus on building and shipping functional code, with possible use of AI tools.
What to demonstrate
- Focus on building and shipping functional code
- With possible use of AI tools
How to prepare
- Answer aloud and timed: Walk us through your approach to integrating a third-party billing or EHR API into a monolithic application.
- Answer aloud and timed: If you were given access to AI-assisted coding tools, how would you leverage them to rapidly scaffold and ship a new microservice?
PracHub editorial advice for the preparation topics above.
Focus on execution over perfection
In the practical coding assessments, prioritize getting a working, end-to-end solution running. The team values engineers who can build and ship software over those who get stuck trying to design a flawless architecture in a limited time window.
Master SQL fundamentals
Do not underestimate the SQL assessment. Practice writing queries that involve complex joins, aggregations, and window functions, as data modeling and retrieval are core components of the evaluation.
Understand the patient journey
Take some time to think about the challenges of mental healthcare delivery. Familiarize yourself with common operational hurdles, such as patient intake, scheduling constraints, and the importance of timely communication (SLAs).
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Merge partitioned event streams into one ordered feed with bounded lateness
The read-model service consumes 64 log partitions carrying about 4,000 events per second in total. Each partition is ordered within itself, but partitions drift by up to 30 seconds, and the activity feed must present a tenant's events in occurred_at order. Produce the merge. State its complexity, the buffer it requires in events and in bytes, what happens when one partition is idle, and what you do with an event that arrives after you have already emitted its position. Payloads average 1 KB.
Approach
- Merge with a min-heap over the 64 partition heads keyed on (occurred_at, event_id): O(log P) per event and O(n log P) overall. The tie-break on event_id is what makes the output deterministic when two partitions carry the same millisecond, which matters because the feed is paginated and a non-deterministic order reorders pages under the reader.
- Emitting the heap head is only correct once every partition has produced everything up to that timestamp, so the emit condition is a watermark: the minimum across partitions of the highest occurred_at seen, less the allowed lateness. Events are held until the watermark passes them, which is what turns individually ordered streams into a jointly ordered one.
- Size the buffer from the lateness rather than guessing: 4,000 events per second times 30 seconds is 120,000 buffered events, and at 1 KB each about 120 MB of heap. That number is the real price of the ordering guarantee and belongs in front of whoever asked for it.
- Handle the idle partition explicitly, because it fails the feed rather than corrupting it: a partition with no traffic never advances its own maximum, so the watermark freezes and output stops entirely. Either every partition emits a periodic idle marker carrying the broker's current time, or the watermark falls back to wall clock for a partition silent beyond a threshold.
Follow-up
- The lateness budget is raised to five minutes. What is the new buffer, and what besides memory changes?
- The consumer restarts. Where does it resume from, and what does the feed look like for the first 30 seconds?
Collapse a redelivered event batch into per-aggregate high-water marks
You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.
Approach
- One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
- Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
- Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
- If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.
Follow-up
- The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
- How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?
Find overlapping job attempts and peak concurrency from lease records
A day of job_run history yields about 50,000,000 attempt records: (job_run_id, job_type, attempt, started_at, finished_at which is NULL when the worker died, lease_expires_at). Leases expire on a clock, so a job that outran its lease ran twice. Produce (a) every job_run_id whose attempts overlapped in wall-clock time and (b) the peak number of simultaneously running attempts per job_type with the minute it occurred. Target O(n log n). State how you treat a NULL finished_at and what clock skew does to your answer.
Approach
- Define the interval before sorting anything: an attempt occupies [started_at, COALESCE(finished_at, lease_expires_at)). finished_at is observed and lease_expires_at is only a promise, so every attempt without a finish contributes an estimate and the whole result is a lower bound on overlap rather than an exact count.
- For peak concurrency, sweep: emit 2n endpoints, sort by (timestamp, kind) with ends ordered before starts at equal timestamps, then walk the sequence maintaining a counter per job_type and record each type's maximum with its timestamp. O(n log n) dominated by the sort, O(n) space, or O(1) extra if the sort is external and the walk streams.
- For overlap detection, do not compare attempts pairwise. A single global sort by (job_run_id, started_at) gives both the grouping and the order; within a group, keep the maximum end seen so far and report an overlap exactly when the next start is less than that running maximum, which is one linear pass after the sort.
- Half-open intervals matter and are easy to get wrong: with closed intervals an attempt ending at the same millisecond another begins reads as concurrency two, and across 50,000,000 records that artefact swamps the real signal.
Follow-up
- A handler is not idempotent and you have found 400 overlapping jobs. Which of them actually caused damage, and what would you query to find out?
- Peak concurrency for one job_type is 4 against a configured cap of 4. Is the cap working, or is the data hiding attempts that never started?
Write a query to identify patients who have not received a follow-up call within their designated 72-hour SLA
Write a query to identify patients who have not received a follow-up call within their designated 72-hour SLA window.
Approach
- Name the grain you start from and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which index the query would use, and what makes it unusable.
- Handle the rows that do not match: that is usually the actual question.
Follow-up
- How does the query change if that join becomes one-to-many?
- What happens to this when the table is ten times larger?
How would you optimize a slow-performing SQL query that retrieves patient appointment histories for our clinic
How would you optimize a slow-performing SQL query that retrieves patient appointment histories for our clinical portal?
Approach
- Name the grain you start from and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which index the query would use, and what makes it unusable.
- Handle the rows that do not match: that is usually the actual question.
Follow-up
- How does the query change if that join becomes one-to-many?
- What happens to this when the table is ten times larger?
Given a database of clinician schedules, write a query to find overlapping or double-booked time slots.
Given a database of clinician schedules, write a query to find overlapping or double-booked time slots.
Approach
- Name the grain you start from and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which index the query would use, and what makes it unusable.
- Handle the rows that do not match: that is usually the actual question.
Follow-up
- How does the query change if that join becomes one-to-many?
- What happens to this when the table is ten times larger?
Design a relational database schema to handle patient medication lists, ensuring proper categorization of drug
Design a relational database schema to handle patient medication lists, ensuring proper categorization of drug classes.
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?
Describe how you would design an automated system to categorize and validate patient medications during the in
Describe how you would design an automated system to categorize and validate patient medications during the intake process.
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 build a resilient notification service that alerts clinical operations when a critical patient S
How would you build a resilient notification service that alerts clinical operations when a critical patient SLA is about to be missed?
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?
Walk us through your approach to integrating a third-party billing or EHR API into a monolithic application.
Walk us through your approach to integrating a third-party billing or EHR API into a monolithic application.
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- 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?
If you were given access to AI-assisted coding tools, how would you leverage them to rapidly scaffold and ship
If you were given access to AI-assisted coding tools, how would you leverage them to rapidly scaffold and ship a new microservice?
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?
Every query on one table stalls for forty seconds mid-deploy
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
- 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.
- 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.
- 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.
- 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?
Built from the rounds and topics Talkiatry candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Talkiatry loop
- Write out the reported sequence: Recruiter Screening, SQL Assessment, Technical Interviews, Final Technical Review.
- 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.
02Work SQL
- Spend the session on SQL, which Talkiatry candidates report being tested on.
- Write one worked example in SQL and time yourself on it.
Deliverable: One timed worked example in SQL.
03Work Healthcare Data (Patient Information)
- Spend the session on Healthcare Data (Patient Information), which Talkiatry candidates report being tested on.
- Write one worked example in Healthcare Data (Patient Information) and time yourself on it.
Deliverable: One timed worked example in Healthcare Data (Patient Information).
04Work Data Modeling
- Spend the session on Data Modeling, which Talkiatry candidates report being tested on.
- Write one worked example in Data Modeling and time yourself on it.
Deliverable: One timed worked example in Data Modeling.
05Answer out loud: SQL & Data Modeling
- Answer aloud, timed: Write a query to identify patients who have not received a follow-up call within their designated 72-hour SLA window.
- Answer aloud, timed: Design a relational database schema to handle patient medication lists, ensuring proper categorization of drug classes.
Deliverable: Spoken answers to 2 reported SQL & Data Modeling question(s), under time.
06Answer out loud: Practical Application & System Design
- Answer aloud, timed: Describe how you would design an automated system to categorize and validate patient medications during the intake process.
- Answer aloud, timed: How would you build a resilient notification service that alerts clinical operations when a critical patient SLA is about to be missed?
Deliverable: Spoken answers to 2 reported Practical Application & System Design question(s), under time.
07Answer out loud: Behavioral & Team Collaboration
- Answer aloud, timed: Tell me about a time you had to deliver a project under a tight deadline in a rapidly changing environment. How did you manage your time and prioritize tasks?
- Answer aloud, timed: How do you handle working with cross-functional team members, such as product managers or clinical staff, who may not have a technical background?
Deliverable: Spoken answers to 2 reported Behavioral & Team Collaboration 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.
Tell me about a time you had to deliver a project under a tight deadline in a rapidly changing environment. Ho
Tell me about a time you had to deliver a project under a tight deadline in a rapidly changing environment. How did you manage your time and prioritize tasks?
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 working with cross-functional team members, such as product managers or clinical staff, who
How do you handle working with cross-functional team members, such as product managers or clinical staff, who may not have a technical background?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Describe a situation where you identified a critical bug or flaw in a production system. How did you communica
Describe a situation where you identified a critical bug or flaw in a production system. How did you communicate the issue and resolve it?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Why are you interested in working in the mental healthcare technology space, and how do you approach the uniqu
Why are you interested in working in the mental healthcare technology space, and how do you approach the unique responsibility of handling patient data?
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
Tell me about a time you had to deliver a project under a tight deadline in a rapidly changing environment. How did you manage your time and prioritize tasks?
- 02
How do you handle working with cross-functional team members, such as product managers or clinical staff, who may not have a technical background?
- 03
Describe a situation where you identified a critical bug or flaw in a production system. How did you communicate the issue and resolve it?
- 04
Why are you interested in working in the mental healthcare technology space, and how do you approach the unique responsibility of handling patient data?
What is the technical stack at Talkiatry?
Talkiatry utilizes modern backend frameworks, relational databases (primarily SQL-based), and cloud infrastructure to power its applications. During the interview process, you are generally welcome to use the programming languages and tools you are most comfortable with, as the focus is on your problem-solving approach and ability to deliver functional code.
Talkiatry Software Engineer candidate reports ↗How much healthcare industry experience do I need?
While prior healthcare experience (such as working with HIPAA, EHRs, or patient data) is a strong plus, it is not a strict requirement. The engineering team values strong core software engineering fundamentals, database proficiency, and a willingness to learn the complexities of the healthcare domain quickly.
Talkiatry Software Engineer candidate reports ↗What is the work environment and remote policy?
Talkiatry offers a highly collaborative working environment, with many engineering roles supporting remote work across the United States. The team relies heavily on tools like Zoom and Slack to maintain strong communication, pair programming, and cross-functional alignment.
Talkiatry Software Engineer candidate reports ↗How quickly does the interview process move?
The interview process typically spans several weeks from the initial recruiter screen to the final round. Talkiatry aims to provide a structured and transparent experience, though candidates are encouraged to maintain open communication with their recruiter regarding timelines and competing offers.
Talkiatry Software Engineer candidate reports ↗What topics does Talkiatry test in interviews?
Talkiatry interviews most often cover Data Modeling, SQL, Product Management (PM), Healthcare Data (Patient Information), and Group Messaging Product Development. The exact emphasis depends on the specific role you apply for.
Talkiatry Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01Talkiatry Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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