At Iterative Health, a Software Engineer does not just write code; they build the critical infrastructure that bridges the gap between fragmented clinical data and life-saving medical research. You will be tasked with designing robust systems that integrate complex, heterogeneous data sources—such as EHRs and CTMS platforms—into a cohesive, actionable foundation for predictive intelligence. This role is essential to our mission of accelerating clinical trials and improving patient outcomes through AI-driven innovation. You will operate at the intersection of high-stakes systems engineering and cutting-edge machine learning. Whether you are building reliable data pipelines, defining long-term infrastructure strategy, or prototyping solutions to de-risk clinical operations, your work will directly influence how our teams bring new therapies to patients. We look for engineers who think in systems, thrive in a fast-moving, formative environment, and possess the technical depth to make foundational decisions that will scale as we grow.
Technical Screening
reportedInitial evaluation to assess technical skills and fit for the role.
What to demonstrate
- Initial evaluation to assess technical skills and fit for the role
- Depth in System Design
How to prepare
- Answer aloud and timed: How would you design a robust integration layer for disparate and unreliable external healthcare data sources?
- Answer aloud and timed: Describe a time you had to build a system from scratch with incomplete information; what were your trade-offs?
Virtual Interviews
reportedSeries of interviews focusing on background, technical design philosophy, and teamwork.
What to demonstrate
- Series of interviews focusing on background, technical design philosophy, and teamwork
- Depth in System Design
How to prepare
- Answer aloud and timed: How do you approach building data pipelines that support production-level ML workloads?
- Answer aloud and timed: Given a specific system architecture, how would you identify and resolve potential bottlenecks or failure points?
Coding Exercises
reportedRigorous coding challenges to evaluate problem-solving skills.
What to demonstrate
- Rigorous coding challenges to evaluate problem-solving skills
- Depth in System Design
How to prepare
- Answer aloud and timed: What considerations do you prioritize when choosing between building a custom solution versus buying an existing vendor tool?
- Answer aloud and timed: Tell me about a time you had to influence a technical decision across product or data science stakeholders.
System Design Discussions
reportedIn-depth discussions on system design and architectural strategy.
What to demonstrate
- In-depth discussions on system design and architectural strategy
- Depth in System Design
How to prepare
- Answer aloud and timed: How do you handle disagreements on technical strategy within your team?
- Answer aloud and timed: Describe an instance where you had to pivot your approach due to shifting operational constraints.
Behavioral Interviews
reportedInterviews focused on communication style, feedback handling, and cultural fit.
What to demonstrate
- Interviews focused on communication style, feedback handling, and cultural fit
- Depth in System Design
How to prepare
- Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
- Re-read the description of the behavioral interviews above and write down what you would ask to confirm before it.
PracHub editorial advice for the preparation topics above.
Structure your answers
Use the STAR method (Situation, Task, Action, Result) for behavioral questions, but ensure your "Action" section highlights the technical trade-offs you considered.
Own your decisions
When discussing past projects, be clear about why you made specific technical choices. We value engineers who can defend their architecture with data and logic.
Know the domain
Read up on the challenges of clinical data integration. Having a baseline understanding of why this is a "hard" problem will set you apart.
Do not focus solely on your experience at large tech companies
We are looking for engineers who can adapt to a small, fast-moving startup environment where you wear many hats.
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?
Identify the heaviest tenants in a five-minute window under memory pressure
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?
Track a rolling failure rate per destination for circuit decisions
The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.
Approach
- Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
- Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
- State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
- Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.
Follow-up
- The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
- A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?
Denormalise tenant onto revisions and backfill it live
resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.
Approach
- Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
- Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
- Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
- Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.
Follow-up
- The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
- How do you verify the backfill actually finished, given rows are still being inserted while it runs?
Replace offset paging on the resource feed with keyset
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
- 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.
- 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.
- 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.
- 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?
How would you design a robust integration layer for disparate and unreliable external healthcare data sources?
How would you design a robust integration layer for disparate and unreliable external healthcare data sources?
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 do you approach building data pipelines that support production-level ML workloads?
How do you approach building data pipelines that support production-level ML workloads?
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?
Given a specific system architecture, how would you identify and resolve potential bottlenecks or failure poin
Given a specific system architecture, how would you identify and resolve potential bottlenecks or failure points?
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 considerations do you prioritize when choosing between building a custom solution versus buying an existi
What considerations do you prioritize when choosing between building a custom solution versus buying an existing vendor tool?
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?
p99 jumped on one listing filter while p50 stayed flat
After a release that added an owner_user_id filter to the resource listing, p99 rose from 90 ms to 1.9 s while p50 stayed at 40 ms. Traffic and row counts are unchanged. resource carries the index (tenant_id, status, updated_at DESC, resource_id DESC). The new query filters tenant_id and owner_user_id, orders by updated_at DESC, resource_id DESC, and takes 20 rows. On PostgreSQL, explain the shape of the regression, prove it from a query plan, and give the index you would add.
Approach
- Start from the shape. A flat p50 with a moved p99 means a subset of requests changed cost, not all of them, so the first job is naming the subset. Bucket the endpoint's latency by the tenant's row count; the natural hypothesis is that large tenants are a small share of requests and all of the tail.
- Get the plan for the new query on a large tenant with EXPLAIN (ANALYZE, BUFFERS). Expect an index scan over the tenant's range, a filter discarding most of it, then a Sort feeding the Limit, possibly reporting Sort Method: external merge Disk. Read actual rows on the scan node, not estimated.
- Explain why the existing index cannot serve it. A composite B-tree is seekable only as a left prefix, and with no equality predicate on status the scan cannot treat updated_at as an ordering, because rows in the tenant's range are ordered by status first. Everything matching must be read and sorted before LIMIT 20 can apply, so a tenant with 400,000 rows pays 400,000 rows to return 20.
- Add (tenant_id, owner_user_id, updated_at DESC, resource_id DESC). Equality on the first two columns leaves the index ordered by updated_at within that pair, so the plan becomes an index scan that stops after 20 rows with no Sort node. PostgreSQL can scan a B-tree backwards, so the DESC markers matter only if the two sort columns ever disagree in direction; keeping them explicit documents the order the keyset cursor depends on.
Follow-up
- The endpoint paginates with OFFSET. What does page 500 cost with your index, and what does the keyset version cost?
- How would you have caught this before release, given that a 10,000-row seed database produces the same plan shape at an unnoticeable cost?
Built from the rounds and topics Iterative Health candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Iterative Health loop
- Write out the reported sequence: Technical Screening, Virtual Interviews, Coding Exercises, System Design Discussions, Behavioral Interviews.
- 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 5 reported rounds, with the weakest marked.
02Work System Design
- Spend the session on System Design, which Iterative Health 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.
03Work Backend Engineering
- Spend the session on Backend Engineering, which Iterative Health candidates report being tested on.
- Write one worked example in Backend Engineering and time yourself on it.
Deliverable: One timed worked example in Backend Engineering.
04Work EHR/EMR Integrations
- Spend the session on EHR/EMR Integrations, which Iterative Health candidates report being tested on.
- Write one worked example in EHR/EMR Integrations and time yourself on it.
Deliverable: One timed worked example in EHR/EMR Integrations.
05Answer out loud: Technical & System Design
- Answer aloud, timed: How would you design a robust integration layer for disparate and unreliable external healthcare data sources?
- Answer aloud, timed: Describe a time you had to build a system from scratch with incomplete information; what were your trade-offs?
Deliverable: Spoken answers to 2 reported Technical & System Design question(s), under time.
06Answer out loud: Behavioral & Leadership
- Answer aloud, timed: Tell me about a time you had to influence a technical decision across product or data science stakeholders.
- Answer aloud, timed: How do you handle disagreements on technical strategy within your team?
Deliverable: Spoken answers to 2 reported Behavioral & Leadership question(s), under time.
07Dry run for Iterative Health
- Run one full mock under time, then write down the two questions you most want to ask your interviewers.
Deliverable: A completed timed mock and two questions to ask.
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.
Describe a time you had to build a system from scratch with incomplete information; what were your trade-offs?
Describe a time you had to build a system from scratch with incomplete information; what were your trade-offs?
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?
Tell me about a time you had to influence a technical decision across product or data science stakeholders.
Tell me about a time you had to influence a technical decision across product or data science stakeholders.
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 disagreements on technical strategy within your team?
How do you handle disagreements on technical strategy within your team?
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 an instance where you had to pivot your approach due to shifting operational constraints.
Describe an instance where you had to pivot your approach due to shifting operational constraints.
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 mentor junior engineers and raise the quality of the codebase through reviews?
How do you mentor junior engineers and raise the quality of the codebase through reviews?
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
Describe a time you had to build a system from scratch with incomplete information; what were your trade-offs?
- 02
Tell me about a time you had to influence a technical decision across product or data science stakeholders.
- 03
How do you handle disagreements on technical strategy within your team?
- 04
Describe an instance where you had to pivot your approach due to shifting operational constraints.
What is the typical timeline from the initial screen to an offer?
The process generally spans 2–3 weeks. We aim to move efficiently while ensuring we have enough touchpoints to get a full picture of your capabilities.
Iterative Health Software Engineer candidate reports ↗Does the interview process vary based on seniority?
Yes. For senior or staff-level roles, we place a much greater emphasis on architectural influence, technical strategy, and your ability to mentor others and shape engineering culture.
Iterative Health Software Engineer candidate reports ↗What is the most common reason candidates do not move forward?
Candidates often struggle when they focus too much on theoretical algorithms and not enough on the practical, "messy" system design problems that are central to our business. Show us your ability to navigate real-world constraints.
Iterative Health Software Engineer candidate reports ↗Is the work environment remote or hybrid?
We are headquartered in Cambridge, MA, and New York City. Please verify the specific location requirements for the role you are applying to, as expectations may vary.
Iterative Health Software Engineer candidate reports ↗What is the interview process for Iterative Health Software Engineer, and how many rounds are there?
Candidates typically start with a Technical Screening, then move through a series of Virtual Interviews. The loop includes Coding Exercises, System Design Discussions, and Behavioral Interviews, based on the step categories used for this role.
Iterative Health Software Engineer candidate reports ↗How difficult is it to get an offer for Iterative Health Software Engineer, based on candidate reports?
In reported interviews for this role at Iterative Health, the most common difficulty rating was average.
Iterative Health Software Engineer candidate reports ↗What coding and system design topics does Iterative Health test for Software Engineer candidates?
Expect evaluation centered on system design and backend-oriented problem solving, with strong emphasis on data integration. The top topics include System Design, Backend Engineering, EHR/EMR Integrations, Data Integration, Python, Data Pipelines, Machine Learning Infrastructure, and Feature Engineering.
Iterative Health Software Engineer candidate reports ↗Does Iterative Health Software Engineer interview include EHR/EMR integration and data pipeline reliability questions?
Yes, the role is explicitly tied to integrating fragmented clinical data, including EHRs and CTMS platforms, into reliable infrastructure. Interview preparation guidance calls out handling unreliable or heterogeneous healthcare data sources, designing for fault tolerance in long-running data pipelines, and maintaining data integrity when integrating third-party systems.
Iterative Health Software Engineer candidate reports ↗What does Iterative Health test about build vs buy and technical trade-offs for Software Engineer?
You should be ready to discuss when to choose custom solutions versus existing vendor tools. The preparation guidance also highlights speed versus durability trade-offs and how to think about technical debt versus feature velocity, especially when requirements or constraints shift.
Iterative Health 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.
- 01Iterative Health 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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