As a Software Engineer at Uptime Crew, you are at the forefront of the AI-driven evolution in software development. You will not just be writing code; you will be architecting the future of how fintech firms build, test, and deploy mission-critical applications. This role is designed for engineers who are eager to master AI-assisted development and apply it to complex, regulated environments where security and reliability are paramount. Your impact will be immediate and measurable. You will join an intensive training program designed to sharpen your skills in Agile methodologies, DevSecOps, and cloud deployment before contributing to high-volume, low-latency systems. By leveraging tools like GitHub Copilot, Cursor, and LangChain, you will accelerate the software development lifecycle, improve test coverage, and ensure that our clients’ infrastructure remains secure and compliant. ##### Tip This role is ideal for candidates who are passionate about emerging AI technologies and want to transition into high-impact fintech engineering roles. Focus your mindset on efficiency, automation, and clean, maintainable code.
Coding Challenge
reportedInitial assessment where candidates complete a coding challenge.
What to demonstrate
- Initial assessment where candidates complete a coding challenge
- Depth in DevSecOps practices
How to prepare
- Answer aloud and timed: How would you implement an efficient search algorithm for a large dataset?
- Answer aloud and timed: Explain the difference between REST and gRPC and when you would choose one over the other.
HR Screen
reportedDiscussion about your background and professional goals with HR.
What to demonstrate
- Discussion about your background and professional goals with HR
- Depth in DevSecOps practices
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.
Build Challenge
reportedA 2-4 hour task to deliver a production-style slice of functionality.
What to demonstrate
- A 2-4 hour task to deliver a production-style slice of functionality
- Depth in DevSecOps practices
How to prepare
- Answer aloud and timed: How do you ensure high test coverage for a new microservice?
- Answer aloud and timed: How have you used AI coding assistants (e.g., Copilot, Cursor) to refactor legacy code?
Technical Deep-Dive
reportedReview your build and discuss your technical choices in detail.
What to demonstrate
- Review your build and discuss your technical choices in detail
- Depth in DevSecOps practices
How to prepare
- Answer aloud and timed: Explain your strategy for verifying the accuracy and security of AI-generated code.
- Answer aloud and timed: How would you use an AI tool to troubleshoot a production log error?
PracHub editorial advice for the preparation topics above.
Document your process
During the build challenge, include a brief README explaining your design decisions and how you utilized AI tools.
Focus on security
Given our fintech focus, always mention security best practices, such as input validation and secure containerization, even if not explicitly asked.
Be prepared to iterate
If an interviewer asks to change a requirement during the technical review, show how you would approach the refactor.
Communicate clearly
We value engineers who can explain complex technical concepts to non-technical stakeholders.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How would you implement an efficient search algorithm for a large dataset?
How would you implement an efficient search algorithm for a large dataset?
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?
Write a function to validate a complex JSON configuration against a security schema.
Write a function to validate a complex JSON configuration against a security schema.
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?
Diff a projection against the primary without per-row point reads
The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.
Approach
- Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
- Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
- For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
- Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.
Follow-up
- The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
- Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?
Write the update path that detects a concurrent edit
resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.
Approach
- One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
- Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
- State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
- Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.
Follow-up
- A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
- Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?
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?
Explain the difference between REST and gRPC and when you would choose one over the other.
Explain the difference between REST and gRPC and when you would choose one over the other.
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?
How do you ensure high test coverage for a new microservice?
How do you ensure high test coverage for 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?
How have you used AI coding assistants (e.g., Copilot, Cursor) to refactor legacy code?
How have you used AI coding assistants (e.g., Copilot, Cursor) to refactor legacy code?
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?
Explain your strategy for verifying the accuracy and security of AI-generated code.
Explain your strategy for verifying the accuracy and security of AI-generated code.
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?
Describe how you would handle a memory leak in a Java or Python application.
Describe how you would handle a memory leak in a Java or Python application.
Approach
- Establish what changed and when, before forming any theory.
- Pick a bisection that eliminates candidates whichever way it turns out.
- Check the instrumentation before believing the symptom.
- Separate the trigger from the cause; the deploy is rarely the bug.
Follow-up
- What would you look at first, and what would it rule out?
- How would you tell a cause from a coincidence here?
How would you use an AI tool to troubleshoot a production log error?
How would you use an AI tool to troubleshoot a production log error?
Approach
- Establish what changed and when, before forming any theory.
- Pick a bisection that eliminates candidates whichever way it turns out.
- Check the instrumentation before believing the symptom.
- Separate the trigger from the cause; the deploy is rarely the bug.
Follow-up
- What would you look at first, and what would it rule out?
- How would you tell a cause from a coincidence here?
Built from the rounds and topics Uptime Crew candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Uptime Crew loop
- Write out the reported sequence: Coding Challenge, HR Screen, Build Challenge, Technical Deep-Dive.
- 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 DevSecOps practices
- Spend the session on DevSecOps practices, which Uptime Crew candidates report being tested on.
- Write one worked example in DevSecOps practices and time yourself on it.
Deliverable: One timed worked example in DevSecOps practices.
03Work Large Language Models (LLMs) for development
- Spend the session on Large Language Models (LLMs) for development, which Uptime Crew candidates report being tested on.
- Write one worked example in Large Language Models (LLMs) for development and time yourself on it.
Deliverable: One timed worked example in Large Language Models (LLMs) for development.
04Work CI/CD
- Spend the session on CI/CD, which Uptime Crew candidates report being tested on.
- Write one worked example in CI/CD and time yourself on it.
Deliverable: One timed worked example in CI/CD.
05Answer out loud: Coding and Technical Proficiency
- Answer aloud, timed: How would you implement an efficient search algorithm for a large dataset?
- Answer aloud, timed: Explain the difference between REST and gRPC and when you would choose one over the other.
Deliverable: Spoken answers to 2 reported Coding and Technical Proficiency question(s), under time.
06Answer out loud: AI-Assisted Development and Tooling
- Answer aloud, timed: How have you used AI coding assistants (e.g., Copilot, Cursor) to refactor legacy code?
- Answer aloud, timed: Explain your strategy for verifying the accuracy and security of AI-generated code.
Deliverable: Spoken answers to 2 reported AI-Assisted Development and Tooling question(s), under time.
07Answer out loud: Problem-Solving and Behavioral
- Answer aloud, timed: Describe a time you had to learn a new technology stack on a tight deadline.
- Answer aloud, timed: How do you handle a situation where a team member disagrees with your proposed technical design?
Deliverable: Spoken answers to 2 reported Problem-Solving and Behavioral 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.
Describe a time you had to learn a new technology stack on a tight deadline.
Describe a time you had to learn a new technology stack on a tight deadline.
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 a situation where a team member disagrees with your proposed technical design?
How do you handle a situation where a team member disagrees with your proposed technical design?
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 identified a security vulnerability in a project and how you addressed it.
Tell me about a time you identified a security vulnerability in a project and how you addressed 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?
- 01
Describe a time you had to learn a new technology stack on a tight deadline.
- 02
How do you handle a situation where a team member disagrees with your proposed technical design?
- 03
Tell me about a time you identified a security vulnerability in a project and how you addressed it.
How long should I spend on the build challenge?
The build challenge is designed to take 2-4 hours. While we value quality, don't over-engineer; focus on meeting the core requirements, including testing and documentation.
Uptime Crew Software Engineer candidate reports ↗Is there a specific AI tool I must use?
We recommend using the tools you are most comfortable with, such as GitHub Copilot, Cursor, or Windsurf. The key is to demonstrate that you can use these tools to augment your own engineering judgment.
Uptime Crew Software Engineer candidate reports ↗What is the interview difficulty level?
Expect a "difficult" rating. The combination of a coding challenge and a build challenge requires both theoretical knowledge and practical execution speed.
Uptime Crew Software Engineer candidate reports ↗Can I request remote work?
No, this role is explicitly on-site at one of our client locations in Brooklyn, Mountain View, San Diego, or Atlanta.
Uptime Crew Software Engineer candidate reports ↗What topics does Uptime Crew test in interviews?
Uptime Crew interviews most often cover DevSecOps practices, Large Language Models (LLMs) for development, CI/CD, AI-assisted coding assistants, and Java. The exact emphasis depends on the specific role you apply for.
Uptime Crew 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.
- 01Uptime Crew 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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