As a Software Engineer at Oscar Insurance, you are at the center of a mission to systematically rebuild health insurance from the ground up. Unlike legacy insurance companies that rely on outdated, fragmented technology, Oscar Insurance operates on a modern, proprietary tech stack designed to make healthcare transparent, simple, and accessible. You will write the code that powers consumer-facing mobile applications, automated claims-processing pipelines, real-time clinical routing, and complex search engines that help members find the right care. Your work directly impacts millions of members, providers, and internal operations teams. By building highly scalable, secure, and resilient systems, you will help dismantle the friction points of the American healthcare system. Whether you are optimizing backend database performance to process millions of medical claims or refining the user interface of the member portal, your engineering decisions will influence clinical outcomes and financial accessibility. The engineering organization at Oscar Insurance operates with a high level of technical rigor, drawing talent from top-tier technology companies. This means you will work alongside highly skilled peers to solve complex distributed systems problems, implement machine learning models, and build robust APIs. It is a fast-paced environment where product empathy is just as critical as algorithmic efficiency.
Recruiter Conversation
reportedInitial discussion to align on your background and team interests.
What to demonstrate
- Initial discussion to align on your background and team interests
- Depth in Coding Exercises (Time-boxed)
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 Assessment
reportedIncludes an online coding challenge or a live technical phone screen.
What to demonstrate
- Includes an online coding challenge or a live technical phone screen
- Depth in Coding Exercises (Time-boxed)
How to prepare
- Answer aloud and timed: Given a list of words and a set of sentences, write an efficient search algorithm to identify and rank sentences containing the target words.
- Answer aloud and timed: Implement a graph traversal algorithm utilizing Breadth-First Search (BFS) or Depth-First Search (DFS) to find the shortest path in a network of medical providers.
Onsite Loop
reportedComprehensive virtual or in-person interviews covering multiple technical sessions.
What to demonstrate
- Comprehensive virtual or in-person interviews covering multiple technical sessions
- Depth in Coding Exercises (Time-boxed)
How to prepare
- Answer aloud and timed: Solve a dynamic programming problem to find the largest sub-matrix containing all 1s in a binary grid.
- Answer aloud and timed: Design a distributed medical claims processing system that can handle sudden spikes in transaction volume while ensuring strict data consistency.
Coding Assessment
reportedTechnical session focused on coding skills.
What to demonstrate
- Technical session focused on coding skills
- Depth in Coding Exercises (Time-boxed)
How to prepare
- Answer aloud and timed: Explain the trade-offs of using multiple database instances, replication strategies, and sharding techniques in a high-throughput environment.
- Answer aloud and timed: Design an API gateway and service infrastructure to support real-time communication between members, providers, and internal insurance operations.
System Design
reportedTechnical session assessing system design capabilities.
What to demonstrate
- Technical session assessing system design capabilities
- Depth in Coding Exercises (Time-boxed)
How to prepare
- Answer aloud and timed: Walk through the architecture of a global search and recommendation engine for healthcare providers, focusing on data ingestion and latency minimization.
- Answer aloud and timed: Walk through a product scenario where a critical bug is discovered in the member application, and analyze how this bug impacts the customer, the providers, and Oscar Insurance operationally.
Product Demo Assessment
reportedCollaborative session to demonstrate product understanding and skills.
What to demonstrate
- Collaborative session to demonstrate product understanding and skills
- Depth in Coding Exercises (Time-boxed)
How to prepare
- Answer aloud and timed: Analyze a case study involving a bottleneck in operations and propose a Python or SQL-based technical solution to optimize the workflow.
- Answer aloud and timed: Evaluate a product design decision for a new telemedicine feature, balancing user experience with regulatory compliance and technical complexity.
Behavioral Wrap-Up
reportedFinal session to discuss behavioral aspects and cultural fit.
What to demonstrate
- Final session to discuss behavioral aspects and cultural fit
- Depth in Coding Exercises (Time-boxed)
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 wrap-up above and write down what you would ask to confirm before it.
3 candidate reports. Individual accounts describe a particular role and hiring cycle.
Oscar Insurance Software Engineer interview: technical funnel to manager screen
After I reached out on LinkedIn, a recruiter contacted me and the process followed a clean series of rounds. First came a technical phone screen. The next stage combined an algorithms-focused screen with a system-design screen, and the final step was a manager screen. Each round seemed to add another lens. It began with core technical ability, then divided into algorithms and system design, befor…
Read full experienceOscar Insurance Software Engineer interview: ghosted after coding
I started with a recruiter call and moved quickly into a LeetCode-style interview. If that went well, I had been told to expect another LeetCode interview and a system-design discussion. I felt I did well across the steps. Then the communication dropped away. I was ghosted after the interviews, and when nothing happened I followed up about a week later. The rest of the process had felt normal, so…
Read full experienceSoftware Engineer interview at Oscar Insurance: timed unfamiliar pattern
A recruiter contacted me a couple of days after I applied and scheduled a technical round about a week later. I had been warned that the questions would be difficult and tightly timed. That proved accurate: I had 30 minutes for a challenging pattern I had never seen before, got partway through it, and could not finish in the time box. The short window made the experience stressful from the beginn…
Read full experiencePracHub editorial advice for the preparation topics above.
Going into the loop without having done this.
Think Aloud During Coding – Interviewers at Oscar Insurance value your problem-solving process just as much as the final working code. If you run into a roadblock, explain your options and why you are choosing a specific path.
Going into the loop without having done this.
Do not jump straight into writing code without clarifying the problem constraints first. Many candidates fail technical screens because they solve the wrong problem or fail to account for critical edge cases discussed early in the call.
Going into the loop without having done this.
Brush Up on Database Fundamentals – System design rounds frequently drill down into the database layer. Be ready to explain exactly why you would choose a specific database engine, how you would index it, and how you would ensure data consistency across services.
Going into the loop without having done this.
Understand the Healthcare Domain – While deep healthcare knowledge is not a strict prerequisite, showing that you understand the unique challenges of the industry—such as data privacy, complex billing systems, and provider networks—will set you apart from other candidates.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Implement a variant of an LRU Cache that handles specific eviction policies and concurrent access patterns.
Implement a variant of an LRU Cache that handles specific eviction policies and concurrent access patterns.
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?
Write an algorithm to reverse a UTF-8 encoded string in place on a provided laptop environment within a strict
Write an algorithm to reverse a UTF-8 encoded string in place on a provided laptop environment within a strict time limit.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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 list of words and a set of sentences, write an efficient search algorithm to identify and rank sentenc
Given a list of words and a set of sentences, write an efficient search algorithm to identify and rank sentences containing the target words.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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?
Implement a graph traversal algorithm utilizing Breadth-First Search (BFS) or Depth-First Search (DFS) to find
Implement a graph traversal algorithm utilizing Breadth-First Search (BFS) or Depth-First Search (DFS) to find the shortest path in a network of medical providers.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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?
Solve a dynamic programming problem to find the largest sub-matrix containing all 1s in a binary grid.
Solve a dynamic programming problem to find the largest sub-matrix containing all 1s in a binary grid.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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?
Evaluate a product design decision for a new telemedicine feature, balancing user experience with regulatory c
Evaluate a product design decision for a new telemedicine feature, balancing user experience with regulatory compliance and technical complexity.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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?
Analyze a case study involving a bottleneck in operations and propose a Python or SQL-based technical solution
Analyze a case study involving a bottleneck in operations and propose a Python or SQL-based technical solution to optimize the workflow.
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?
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?
Design a distributed medical claims processing system that can handle sudden spikes in transaction volume whil
Design a distributed medical claims processing system that can handle sudden spikes in transaction volume while ensuring strict data consistency.
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?
Explain the trade-offs of using multiple database instances, replication strategies, and sharding techniques i
Explain the trade-offs of using multiple database instances, replication strategies, and sharding techniques in a high-throughput environment.
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?
Design an API gateway and service infrastructure to support real-time communication between members, providers
Design an API gateway and service infrastructure to support real-time communication between members, providers, and internal insurance operations.
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 through the architecture of a global search and recommendation engine for healthcare providers, focusing
Walk through the architecture of a global search and recommendation engine for healthcare providers, focusing on data ingestion and latency minimization.
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 through a product scenario where a critical bug is discovered in the member application, and analyze how
Walk through a product scenario where a critical bug is discovered in the member application, and analyze how this bug impacts the customer, the providers, and Oscar Insurance operationally.
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Read latency spikes on a sixty-second sawtooth
The cached listing read path serves about 14k reads/second at an 85% hit rate. p99 sits at 35 ms for 57 seconds, jumps to 900 ms for 3, and repeats. During each spike the primary shows several hundred identical listing queries starting within the same millisecond, all carrying one large tenant's id. Cache entries use a 60-second TTL. Give the mechanism, the ordered checks, the fix, and the correctness hazard your fix must not introduce.
Approach
- Match the period to a configured number before theorising about load. A spike every 60 seconds against a 60-second TTL is an entry expiring, and you confirm it by correlating spike timestamps with the entry's write time rather than with the traffic curve. If the period had matched a cron or a GC interval instead, this is a different investigation.
- Establish the concurrency of the miss. Several hundred identical queries in one millisecond means the miss path has no coalescing: every request that arrives between expiry and repopulation recomputes. The herd size is that key's arrival rate times its recompute time, so at 1.2k reads/second for the hot key and a 250 ms recompute you expect about 300 concurrent misses, which matches what is observed.
- Add single-flight on the miss path so one caller per key recomputes under a short-lived lock while the rest wait for its result. Prefer stale-while-revalidate where the read tolerates it: return the expired value immediately and refresh asynchronously, which removes the latency spike rather than serialising it into a queue of waiters.
- De-synchronise the keys. Write TTLs with jitter, for example 60 seconds plus or minus 10%, so a deploy or a mass invalidation does not align every key on the same second and turn a per-key herd into a fleet-wide one.
Follow-up
- The same sawtooth appears on a key that is invalidated on write rather than expired. Is that the same bug?
- How does your answer change if the recompute takes 4 seconds instead of 250 ms?
Built from the rounds and topics Oscar Insurance candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Oscar Insurance loop
- Write out the reported sequence: Recruiter Conversation, Technical Assessment, Onsite Loop, Coding Assessment, System Design, Product Demo Assessment.
- 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 7 reported rounds, with the weakest marked.
02Work Coding Exercises (Time-boxed)
- Spend the session on Coding Exercises (Time-boxed), which Oscar Insurance candidates report being tested on.
- Write one worked example in Coding Exercises (Time-boxed) and time yourself on it.
Deliverable: One timed worked example in Coding Exercises (Time-boxed).
03Work Algorithmic Problem Solving
- Spend the session on Algorithmic Problem Solving, which Oscar Insurance candidates report being tested on.
- Write one worked example in Algorithmic Problem Solving and time yourself on it.
Deliverable: One timed worked example in Algorithmic Problem Solving.
04Work System Design
- Spend the session on System Design, which Oscar Insurance 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.
05Answer out loud: Algorithms and Data Structures
- Answer aloud, timed: Implement a variant of an LRU Cache that handles specific eviction policies and concurrent access patterns.
- Answer aloud, timed: Write an algorithm to reverse a UTF-8 encoded string in place on a provided laptop environment within a strict time limit.
Deliverable: Spoken answers to 2 reported Algorithms and Data Structures question(s), under time.
06Answer out loud: System Design and Architecture
- Answer aloud, timed: Design a distributed medical claims processing system that can handle sudden spikes in transaction volume while ensuring strict data consistency.
- Answer aloud, timed: Explain the trade-offs of using multiple database instances, replication strategies, and sharding techniques in a high-throughput environment.
Deliverable: Spoken answers to 2 reported System Design and Architecture question(s), under time.
07Answer out loud: Product Case Studies and Operations
- Answer aloud, timed: Walk through a product scenario where a critical bug is discovered in the member application, and analyze how this bug impacts the customer, the providers, and Oscar Insurance operationally.
- Answer aloud, timed: Analyze a case study involving a bottleneck in operations and propose a Python or SQL-based technical solution to optimize the workflow.
Deliverable: Spoken answers to 2 reported Product Case Studies and Operations 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.
Ship under a deadline and bound the debt you chose
You have four days to ship a tenant-facing listing endpoint. The version you would defend uses keyset pagination over (tenant_id, status, updated_at DESC, resource_id DESC); the version you can finish uses LIMIT/OFFSET with no matching index. Describe a deadline call you actually made of this shape: what you shipped, what you knowingly deferred, how you bounded the damage with a mechanism rather than an intention, and the specific numeric condition that would force the follow-up. Name who you told and where you wrote it down.
Approach
- Name the deferred failure precisely instead of calling it slow. OFFSET n makes the database produce and discard n rows, so cost grows with page depth; without an index matching the sort, every matching row is read and sorted before the limit applies; and rows inserted between two page fetches shift across the boundary so items are skipped or repeated with nothing in the response to signal it.
- Bound the blast radius with something mechanical rather than a promise: cap maximum page depth, cap page size, restrict the endpoint to one internal caller, or keep it behind a flag. State which failure each cap removes and which it leaves standing.
- Attach a number to the trigger and wire it to an alarm: the first tenant crossing N resources, or the endpoint's p99 crossing its share of the 400 ms budget, so the debt announces itself instead of waiting to be remembered.
- Write it where the next engineer looks, which is the code and the ticket, not a chat message: what was deferred, why, the cap, and the trigger.
Follow-up
- At what page depth does the offset version breach your latency budget, given your page size and row counts?
- What breaks first when you switch to keyset pagination later, and what does a client holding an old page token see?
Reverse your own decision and price the reversal
Describe a technical decision you made and later reversed. Pick one that cost something: a service you split and merged back, a cache you added and removed, an index you created that pushed the planner onto a worse plan, a projection you rebuilt from scratch. State what you believed when you decided, the measurement that changed your mind, how long the wrong version ran in production, and what the reversal cost in migrations, dual writes, and a deprecation window for callers you did not own.
Approach
- State the original rationale without irony, in the version you would still defend given what was known then. If it is not defensible, the story is about carelessness rather than judgement, and a different example serves you better.
- Give the measurement that moved with a before and after: the p99 that did not improve, the cache hit rate that sat at 40%, the plan that flipped to a sequential scan once the table passed a size you can name.
- Cost the reversal in steps, not adjectives: expand-and-contract deploys, the dual-write window, the callers who had to be notified, the rows already written in the wrong shape that had to be backfilled or abandoned.
- Distinguish reversal from rewrite by naming what you kept. Most good reversals preserve the schema or the interface and undo one decision inside it, which is also why they were affordable.
Follow-up
- What in that decision was irreversible, and did you know it was irreversible when you made it?
- How did you tell the people who had already built on top of the original decision?
Turn a code review disagreement into a decision
A colleague's change updates a row with UPDATE resource SET version = version + 1 WHERE resource_id = $1 AND version = $2 and treats an affected-row count of zero as a successful no-op. You read that as a silently lost update; they think returning 200 is friendlier to clients than returning a conflict. Describe how you have handled a review disagreement of this shape: what goes in the comment, when you leave the thread, and who decides. Then write the comment you would leave here, in under 80 words.
Approach
- Sort the disagreement before writing anything. A silently discarded write is a correctness claim about data; the choice between 409 and 412 is taste. Only the first justifies blocking a merge, and saying which one you are doing is most of the value of the comment.
- Make the claim reproducible in the comment itself with an interleaving rather than a principle: A reads version 7, B reads version 7, B commits version 8, A's predicate matches zero rows, A is told it succeeded and A's edit is gone.
- Offer the alternative with its cost attached: return 409 carrying the current version and the revision that won, so the client can re-read and re-apply. Note that automatic retry is not the fix, because a retry re-reads the winner's state and reapplies an intent formed against data that no longer exists.
- Apply an escalation rule you can state: two round trips on the thread, then a call, and the service's owner decides rather than the reviewer. A reviewer who cannot be overruled is a bottleneck with extra steps.
Follow-up
- Where would you put the test that fails if someone reintroduces the swallowed zero rowcount?
- The author says clients cannot handle a 409. How do you check whether that is true?
- 01
You have four days to ship a tenant-facing listing endpoint. The version you would defend uses keyset pagination over (tenant_id, status, updated_at DESC, resource_id DESC); the version you can finish uses LIMIT/OFFSET with no matching index. Describe a deadline call you actually made of this shape: what you shipped, what you knowingly deferred, how you bounded the damage with a mechanism rather than an intention, and the specific numeric condition that would force the follow-up. Name who you told and where you wrote it down.
- 02
Describe a technical decision you made and later reversed. Pick one that cost something: a service you split and merged back, a cache you added and removed, an index you created that pushed the planner onto a worse plan, a projection you rebuilt from scratch. State what you believed when you decided, the measurement that changed your mind, how long the wrong version ran in production, and what the reversal cost in migrations, dual writes, and a deprecation window for callers you did not own.
- 03
A colleague's change updates a row with UPDATE resource SET version = version + 1 WHERE resource_id = $1 AND version = $2 and treats an affected-row count of zero as a successful no-op. You read that as a silently lost update; they think returning 200 is friendlier to clients than returning a conflict. Describe how you have handled a review disagreement of this shape: what goes in the comment, when you leave the thread, and who decides. Then write the comment you would leave here, in under 80 words.
How difficult is the Software Engineer interview at Oscar Insurance?
The technical bar is high and is comparable to top-tier technology companies. You should expect challenging algorithmic coding questions and deep, practical system design discussions that test real-world trade-offs rather than theoretical concepts.
Oscar Insurance Software Engineer candidate reports ↗What is the typical timeline for the interview process?
The entire process typically takes between 3 to 6 weeks from the initial recruiter call to the final decision. However, scheduling delays can sometimes occur, so candidates are encouraged to maintain active communication with their recruiting coordinator.
Oscar Insurance Software Engineer candidate reports ↗Does Oscar Insurance hire remote engineers?
Yes, Oscar Insurance offers remote positions, but many of these roles are strictly limited to candidates residing and working within the United States. Be sure to clarify location requirements with your recruiter during the initial call.
Oscar Insurance Software Engineer candidate reports ↗How should I prepare for the Product Demo round?
Approach this round with a product manager's mindset. Focus on how technical systems serve business goals and user needs, and be prepared to discuss how you would measure the success and impact of your engineering decisions.
Oscar Insurance Software Engineer candidate reports ↗How hard is the Oscar Insurance interview?
Candidates most commonly rate Oscar Insurance interviews as medium, based on 505 reported interviews. About 26% of candidates who interview go on to receive an offer.
Oscar Insurance Software Engineer candidate reports ↗What topics does Oscar Insurance test in interviews?
Oscar Insurance interviews most often cover Behavioral Interviewing, Communication Skills, System Design, SQL, and UI Design. The exact emphasis depends on the specific role you apply for.
Oscar Insurance Software Engineer candidate reports ↗Where is Oscar Insurance headquartered?
Oscar Insurance is headquartered in New York, NY.
Oscar Insurance Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01Oscar Insurance Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
candidate · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
platform · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
platform · Accessed 2026-09-22
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