Interview guides · Develop Health

Develop Health Software Engineer Interview Guide

Develop Health Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

At Develop Health, a Software Engineer is not just writing code; they are building the core infrastructure that directly impacts patient outcomes and modernizes healthcare delivery. The company operates in a highly critical, fast-paced domain where artificial intelligence, machine learning, and secure data streaming intersect. Engineers here are responsible for designing, building, and optimizing full-stack applications and data pipelines that handle sensitive medical information and automate complex healthcare workflows. This role is highly autonomous and requires a strong entrepreneurial spirit. As a member of a fast-growing, AI-forward engineering team, you will collaborate closely with the founders, product managers, and clinical experts to translate complex regulatory and medical requirements into elegant, high-performance software.

This guide is scoped to a Software Engineer candidate at Develop Health.

Focus areas

Python (backend/API development)
Machine Learning (ML) model inference in applications
Natural Language Processing (NLP) / Q&A models

The Develop Health interview process

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

Recruiter Conversation

Initial conversation with a recruiter or co-founder to align on goals, culture, and company objectives.

What to demonstrate

Initial conversation with a recruiter or co-founder to align on goals, culture, and company objectives
Depth in Python (backend/API development)

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 Screening Call

Call that explores your knowledge of system design, data streaming, and AI concepts.

What to demonstrate

Call that explores your knowledge of system design, data streaming, and AI concepts
Depth in Python (backend/API development)

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.

Take-Home Assignment

Core evaluation step where candidates complete a take-home project.

What to demonstrate

Core evaluation step where candidates complete a take-home project
Depth in Python (backend/API development)

How to prepare

Answer aloud and timed: What are the most common issues associated with streaming data, and how do you mitigate them in a production pipeline?
Answer aloud and timed: How would you design a system to process real-time healthcare data streams while ensuring zero data loss?

Onsite Interview Day

Includes a review of your solution, collaborative coding, and informal team conversations.

What to demonstrate

Includes a review of your solution, collaborative coding, and informal team conversations
Depth in Python (backend/API development)

How to prepare

Answer aloud and timed: How do you approach database schema design for an application that needs to store both structured patient records and unstructured AI-generated insights?
Answer aloud and timed: What architectural patterns would you implement to separate computationally heavy AI inference tasks from lightweight web API routing?

Where candidates lose points

Test Your Environments Early

When working on the take-home assignment, set up your local development environment and test any provided boilerplate repository as soon as possible. If you encounter any runtime issues, model download failures, or dependency conflicts, proactively reach out to the engineering team for clarification.

Focus on the "Why

During the take-home review and system design discussions, always explain the reasoning behind your choices. Whether you chose a specific library, database schema, or error-handling pattern, being able to articulate your trade-offs is highly valued.

Show Your Product Intuition

Develop Health values engineers who think about the end-user. When designing features or writing code, consider how your technical decisions impact the final patient or clinician experience.

Prepare Questions for the Founders

The interview process often includes direct conversations with the founders. Prepare thoughtful questions about the company's long-term vision, product roadmap, and how they navigate the evolving landscape of AI in healthcare.

Questions & practice

What strategies would you use to optimize the latency of a machine learning model that downloads and runs at r

Medium · AI Integration & Practical Coding

What strategies would you use to optimize the latency of a machine learning model that downloads and runs at runtime?

Approach

Say what the runtime actually does before reasoning about the code.
Name what is shared across threads and what owns each piece of state.
Identify the window where an invariant is briefly untrue.
Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

What happens if two callers reach this at the same time?
Where could this allocate more than you expect?

Explain how you would structure a Python application to ensure that runtime dependencies and model downloads d

Medium · AI Integration & Practical Coding

Explain how you would structure a Python application to ensure that runtime dependencies and model downloads do not block the main application thread.

Approach

Say what the runtime actually does before reasoning about the code.
Name what is shared across threads and what owns each piece of state.
Identify the window where an invariant is briefly untrue.
Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

What happens if two callers reach this at the same time?
Where could this allocate more than you expect?

Identify the heaviest tenants in a five-minute window under memory pressure

Medium · top-k · heavy hitters · streaming

The edge service handles about 3,000 requests per second across roughly 50,000 tenants, peaking near 9,000. Expose the 50 heaviest tenants by request count over the trailing five minutes so limits can be tightened before one tenant's backfill starves the fleet. You may not retain five minutes of raw records. Give the exact solution and its memory, then the bounded-memory approximation with its error stated as a formula, and say which you would ship and at what tenant cardinality that choice changes.

Approach

Do the exact version first, because it is affordable at this cardinality: a ring of 300 one-second counters per tenant, advanced lazily, is 1,200 bytes of counters per tenant and roughly 60 to 90 MB for 50,000 tenants with overhead. Carry a running total and subtract the bucket you overwrite so a window read is O(1) rather than 300 adds.
Extract the top 50 with a size-k min-heap over the tenant sums: O(d log k) for d tenants, against O(d log d) to sort them all. Maintaining the heap continuously instead of on query requires a tenant-to-heap-index map, because incrementing a count already inside the heap means sifting from a known position, and without that map you rebuild the heap on every request.
State the approximation precisely rather than gesturing at sketches. Misra-Gries with m counters retains every item whose true count exceeds N/(m+1), and each retained count underestimates the truth by at most N/(m+1). With m = 1,000 and N = 900,000 requests in the window the error is roughly 900 requests, which is fine for spotting a tenant sending 50,000 and useless for ranking two tenants 200 apart.
Say what breaks when the window slides: Misra-Gries and Space-Saving are insert-only and cannot be decremented as records age out. The workable construction is one summary per sub-window, say ten seconds, with 30 summaries merged at query time, and the merged error is the sum of the per-summary errors, so the bound degrades linearly in the number of sub-windows.

Follow-up

The heaviest tenant is heavy because of one export job rather than user traffic. Should the limiter treat those as the same tenant?
Two tenants sit tied at the boundary of the top 50. Does your answer flap, and does the flapping matter?

How do you approach database schema design for an application that needs to store both structured patient reco

Medium · Systems, Data Streaming & Architecture

How do you approach database schema design for an application that needs to store both structured patient records and unstructured AI-generated insights?

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?

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

Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.

Follow-up

90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
A dashboard runs this for 40 owners in one page load. What changes about the design?

How would you design a Python API endpoint that receives client data, processes it through a Q&A-trained model

Medium · AI Integration & Practical Coding

How would you design a Python API endpoint that receives client data, processes it through a Q&A-trained model, and returns structured answers?

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?

What are the most common issues associated with streaming data, and how do you mitigate them in a production p

Medium · Systems, Data Streaming & Architecture

What are the most common issues associated with streaming data, and how do you mitigate them in a production pipeline?

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

How would you design a system to process real-time healthcare data streams while ensuring zero data loss?

Medium · Systems, Data Streaming & Architecture

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?

What architectural patterns would you implement to separate computationally heavy AI inference tasks from ligh

Medium · Systems, Data Streaming & Architecture

What architectural patterns would you implement to separate computationally heavy AI inference tasks from lightweight web API routing?

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?

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

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.
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.
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.
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 Develop Health candidates report.

Day 1 — Map the Develop Health loop

Write out the reported sequence: Recruiter Conversation, Technical Screening Call, Take-Home Assignment, Onsite Interview Day.
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 Python (backend/API development)

Spend the session on Python (backend/API development), which Develop Health candidates report being tested on.
Write one worked example in Python (backend/API development) and time yourself on it.

Deliverable: One timed worked example in Python (backend/API development).

Day 3 — Work Machine Learning (ML) model inference in applications

Spend the session on Machine Learning (ML) model inference in applications, which Develop Health candidates report being tested on.
Write one worked example in Machine Learning (ML) model inference in applications and time yourself on it.

Deliverable: One timed worked example in Machine Learning (ML) model inference in applications.

Day 4 — Work Natural Language Processing (NLP) / Q&A models

Spend the session on Natural Language Processing (NLP) / Q&A models, which Develop Health candidates report being tested on.
Write one worked example in Natural Language Processing (NLP) / Q&A models and time yourself on it.

Deliverable: One timed worked example in Natural Language Processing (NLP) / Q&A models.

Day 5 — Answer out loud: AI Integration & Practical Coding

Answer aloud, timed: How would you design a Python API endpoint that receives client data, processes it through a Q&A-trained model, and returns structured answers?
Answer aloud, timed: What strategies would you use to optimize the latency of a machine learning model that downloads and runs at runtime?

Deliverable: Spoken answers to 2 reported AI Integration & Practical Coding question(s), under time.

Day 6 — Answer out loud: Systems, Data Streaming & Architecture

Answer aloud, timed: What are the most common issues associated with streaming data, and how do you mitigate them in a production pipeline?
Answer aloud, timed: How would you design a system to process real-time healthcare data streams while ensuring zero data loss?

Deliverable: Spoken answers to 2 reported Systems, Data Streaming & Architecture question(s), under time.

Day 7 — Answer out loud: Behavioral, Culture & Domain Alignment

Answer aloud, timed: Why do you want to work at the intersection of AI, machine learning, and healthcare?
Answer aloud, timed: Describe a time when you had to work with an ambiguous code repository or a third-party API that did not work as expected. How did you resolve the situation?

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

Behavioural preparation

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

How do you handle failure states and API timeouts when communicating with external AI/ML services?

Medium · AI Integration & Practical Coding

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 do you want to work at the intersection of AI, machine learning, and healthcare?

Medium · Behavioral, Culture & Domain Alignment

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 time when you had to work with an ambiguous code repository or a third-party API that did not work

Medium · Behavioral, Culture & Domain Alignment

Describe a time when you had to work with an ambiguous code repository or a third-party API that did not work as expected. How did you resolve the situation?

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 balance the need for rapid feature development with the strict security and compliance requirements

Medium · Behavioral, Culture & Domain Alignment

How do you balance the need for rapid feature development with the strict security and compliance requirements of the healthcare industry?

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?

Share an experience where you had to explain a complex technical trade-off to a non-technical stakeholder or c

Medium · Behavioral, Culture & Domain Alignment

Share an experience where you had to explain a complex technical trade-off to a non-technical stakeholder or co-founder.

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?