As a Software Engineer at Noom, you are at the intersection of consumer technology and behavioral health. Your work directly impacts millions of users, helping them build sustainable, healthy habits through data-driven insights and personalized coaching experiences. Whether you are scaling our infrastructure to support rapid growth or building intuitive features that keep users engaged, your code is the engine behind our mission. This role requires a unique blend of technical rigor and user empathy. You will work within cross-functional teams to solve complex problems—from optimizing data pipelines for health tracking to architecting scalable systems that handle high-concurrency requests. You aren't just writing code; you are contributing to a product that changes lives, requiring you to think deeply about system reliability, performance, and the long-term maintainability of our codebase.
Recruiter Screen
reportedInitial screening with a recruiter to discuss your background and fit for the role.
What to demonstrate
- Initial screening with a recruiter to discuss your background and fit for the role
- Depth in Data Structures & Algorithms
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 Assessments
reportedSeries of technical assessments evaluating coding speed, accuracy, and architectural judgment.
What to demonstrate
- Series of technical assessments evaluating coding speed, accuracy, and architectural judgment
- Depth in Data Structures & Algorithms
How to prepare
- Answer aloud and timed: Given a set of fitness tracker data, design a data structure that tracks mile/km splits and accounts for pauses in a workout.
- Answer aloud and timed: Explain your choice of data structures for a given problem and justify the algorithmic complexity.
Power Day
reportedFinal virtual onsite where candidates are evaluated in a collaborative, high-pressure environment.
What to demonstrate
- Final virtual onsite where candidates are evaluated in a collaborative, high-pressure environment
- Depth in Data Structures & Algorithms
How to prepare
- Answer aloud and timed: Design a URL shortening service that can handle high traffic.
- Answer aloud and timed: How would you architect a system for generating large-scale coach responsibility reports?
PracHub editorial advice for the preparation topics above.
Think Out Loud
Your interviewer is more interested in your thought process than your ability to arrive at the "perfect" answer in silence. Verbalize your assumptions and your logic as you work.
Practice Mock Interviews
The live-coding environment can be nerve-wracking. Conduct mock sessions with peers to get comfortable with the pressure of being watched while you type.
Clarify First
Never start coding until you have confirmed the input types, edge cases, and constraints. This is the most common pitfall in our technical screens.
Know Your Resume
Be prepared to dive deep into any project you list on your resume. You should be able to explain the "why" behind every technical decision you made in those projects.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Implement a function to calculate the average time spent on a specific task across a list of user logs.
Implement a function to calculate the average time spent on a specific task across a list of user logs.
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?
Identify common elements between two large lists and discuss how your solution would scale.
Identify common elements between two large lists and discuss how your solution would scale.
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?
Explain your choice of data structures for a given problem and justify the algorithmic complexity.
Explain your choice of data structures for a given problem and justify the algorithmic 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?
Describe how you would scale a data-intensive feature to support millions of concurrent users.
Describe how you would scale a data-intensive feature to support millions of concurrent users.
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?
Discuss the trade-offs between different database schemas for a high-frequency tracking application.
Discuss the trade-offs between different database schemas for a high-frequency tracking application.
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?
Given a set of fitness tracker data, design a data structure that tracks mile/km splits and accounts for pause
Given a set of fitness tracker data, design a data structure that tracks mile/km splits and accounts for pauses in a workout.
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 a URL shortening service that can handle high traffic.
Design a URL shortening service that can handle high traffic.
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 architect a system for generating large-scale coach responsibility reports?
How would you architect a system for generating large-scale coach responsibility reports?
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?
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 Noom candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Noom loop
- Write out the reported sequence: Recruiter Screen, Technical Assessments, Power 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 3 reported rounds, with the weakest marked.
02Work Data Structures & Algorithms
- Spend the session on Data Structures & Algorithms, which Noom candidates report being tested on.
- Write one worked example in Data Structures & Algorithms and time yourself on it.
Deliverable: One timed worked example in Data Structures & Algorithms.
03Work System Design
- Spend the session on System Design, which Noom 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.
04Work Coding Interviews (Live Coding)
- Spend the session on Coding Interviews (Live Coding), which Noom candidates report being tested on.
- Write one worked example in Coding Interviews (Live Coding) and time yourself on it.
Deliverable: One timed worked example in Coding Interviews (Live Coding).
05Answer out loud: Coding and Algorithms
- Answer aloud, timed: Implement a function to calculate the average time spent on a specific task across a list of user logs.
- Answer aloud, timed: Identify common elements between two large lists and discuss how your solution would scale.
Deliverable: Spoken answers to 2 reported Coding and Algorithms question(s), under time.
06Answer out loud: System Design
- Answer aloud, timed: Design a URL shortening service that can handle high traffic.
- Answer aloud, timed: How would you architect a system for generating large-scale coach responsibility reports?
Deliverable: Spoken answers to 2 reported System Design question(s), under time.
07Answer out loud: Behavioral and Problem Solving
- Answer aloud, timed: Describe a time you had to deal with ambiguous project requirements.
- Answer aloud, timed: How do you handle feedback on your code, especially when your solution is challenged?
Deliverable: Spoken answers to 2 reported Behavioral and Problem Solving 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 deal with ambiguous project requirements.
Describe a time you had to deal with ambiguous project requirements.
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 feedback on your code, especially when your solution is challenged?
How do you handle feedback on your code, especially when your solution is challenged?
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 pivot your technical strategy due to changing project needs.
Tell me about a time you had to pivot your technical strategy due to changing project needs.
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 deal with ambiguous project requirements.
- 02
How do you handle feedback on your code, especially when your solution is challenged?
- 03
Tell me about a time you had to pivot your technical strategy due to changing project needs.
How long does the entire interview process usually take?
The process can range from a few weeks to over a month, depending on team availability and the seniority of the role. We aim for transparency, but please feel free to check in with your recruiter if you haven't heard back within the expected timeframe.
Noom Software Engineer candidate reports ↗Is the coding interview done on a whiteboard or a computer?
Most technical interviews are conducted remotely using collaborative coding environments. You should practice writing code in a shared text editor without the help of a full IDE.
Noom Software Engineer candidate reports ↗What is the best way to prepare for the "problem-solving" rounds?
These rounds often focus on algorithmic complexity. Practice optimizing your code for large-scale data sets and be ready to discuss how your solution would perform under heavy load.
Noom Software Engineer candidate reports ↗Does Noom hire remote engineers?
Yes, we have a distributed workforce, though some roles may have specific location or time-zone requirements based on the team's needs.
Noom Software Engineer candidate reports ↗What topics does Noom test in interviews?
Noom interviews most often cover SQL, SQL Aggregations, Communication Skills, Problem Solving, and Stakeholder Management. The exact emphasis depends on the specific role you apply for.
Noom 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.
- 01Noom 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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