Went through the full Anduril SWE loop for an L4 embedded role on their autonomous systems team.

Recruiter Screen

30 minutes. Standard stuff but with a twist, they actually asked if I had reservations about working in defense. Not a trap, they want to filter out people who'll have moral crises three months in. I said I was fine with it, we moved on. She also asked about clearance eligibility which is just "are you a US citizen" at this stage.

Technical Phone Screen

One hour, CoderPad. The problem was framed as managing a fleet of drones with task priority queues, so essentially a modified heap problem but with constraints around real-time task reassignment when a drone goes offline. The algorithmic core was maybe LC medium but the follow-ups about scaling and fault tolerance pushed it harder. The interviewer kept asking "what happens when one drone loses signal mid-task" and I had to keep adapting my solution. Less about getting the optimal answer, more about how I handled changing requirements on the fly.

Onsite Day (4 hours)

Coding Round 1: Graph problem. Given a network of sensor nodes that can relay signals to each other within a certain radius, find the minimum set of nodes needed to maintain full coverage if K nodes fail. I went with a modified minimum vertex cover approach and it worked but my initial solution was O(n³) and the interviewer pushed me to optimize. Got it down to O(n² log n) with a priority-based greedy approach. He seemed happy enough.

Coding Round 2: More practical. Parse a telemetry log file with corrupted entries, reconstruct the valid data stream, and flag anomalies. This was more of a real engineering problem than an algorithms puzzle, lots of string processing and edge case handling. I actually enjoyed this one because it felt like something you'd actually do at work instead of an artificial contest problem.

System Design: Design a command and control system for coordinating autonomous drones across a contested network where communication can be jammed or delayed. This was incredible honestly. The interviewer was a staff engineer who clearly works on this stuff daily and he kept injecting realistic failure modes, "what if the satellite link drops for 30 seconds during a critical operation." I talked through eventual consistency models, local decision-making fallbacks, and how to handle conflicting state when communication resumes. Best system design round I've ever done because it was genuinely interesting instead of "design Twitter" for the 50th time.

Behavioral: Why defense tech, a conflict story, and a question about working under time pressure with real consequences. They're clearly screening for people who take the work seriously since their software literally controls weapons systems.

Prep that helped: Neetcode for the algorithmic basics, but honestly the biggest difference was practicing live system design conversations out loud. The Anduril interviewers don't give you 5 minutes of silence to think, they want a back-and-forth conversation the entire time. Definitely read up on their Lattice platform before interviewing, they will ask you about it.

Got the offer. Comp was competitive with mid-level FAANG. AMA if anyone's considering defense tech.

I love this shit.

Gave a tech screen today but could only finish the first question with a working solution and couldn’t get to the second question due to lack of time. Can I still make it to onsite ?

5 YOE, decided to cheat my way through my interview loops because I couldn't afford another round of rejections. Used Ultracode on one loop, got caught in a coding round (nothing said, no email, no ban, just no offer). A couple months later I had a different loop coming up and gave Interview Coder a shot. Got the job.

Pricing IC is $299/month or $799 lifetime. Ultracode is $899 to $1,799 one-time, no monthly plan, non-refundable. If your loop is 2 to 3 weeks long IC is dramatically cheaper. The only argument for Ultracode's pricing is if you're going to be interviewing nonstop for a year, and even then IC's lifetime tier is cheaper.

Detection This is where Ultracode lost me. Coding round, I'm a few minutes in, the interviewer goes quiet for a long stretch and then starts asking pointed follow-ups about my approach in a way that didn't feel routine. Polite rejection a few days later. Before my next loop I tested IC on Zoom, Meet, and CoderPad with QuickTime recording in parallel. Invisible across all three. Could have been my own setup with Ultracode, could have been the tool. I'm not the one with the marketing page claiming kernel-level integration.

Quality of help IC is more decisive on actual coding rounds. Single approach, surfaced fast, easy to talk through. Ultracode hedges and dumps more output, which slowed me down and made it harder to explain my reasoning when the interviewer asked me to walk through what I just typed. On system design IC has a real module, Ultracode's design support is thinner.

Use IC.

PS - Ultracode's founders are anonymous, the domain is literally flagged on ScamAdviser for hidden ownership. Saw some Blind threads about a breach situation where a chunk of people who used it ended up getting found out. Roy Lee at IC isn't hiding, Wikipedia page, Amazon loop on YouTube, the whole story is public. Worth knowing before you put $1,799 non-refundable on a team that won't tell you who they are.

Anyone has interviewcoder subscription wants to rent it to me for few weeks? As I need it for few weeks doesn’t make sense to purchase such expensive plans.

The questions we get most in this sub are: what is InterviewCoder, how does it work, and how do the proctoring platforms catch people. This post covers all three. Structure: (1) what the product is and how to use it, (2) how HackerRank tracks candidates in 2026, (3) how CodeSignal tracks candidates, (4) where the detection has blind spots, (5) practical advice whether or not you use a tool, (6) why it was built and the product itself.

Part 1. What InterviewCoder is and how to use it

InterviewCoder is a desktop application for macOS and Windows that runs as an overlay during technical interviews and online assessments. It listens to the interviewer's audio (or reads the on-screen problem), runs the question through an AI model, and displays a solution outline, code, and walkthrough in a transparent overlay that is not captured by screen-share or screen-recording.

The architecture rests on four properties:

• The window is excluded from display capture at the OS compositor level (macOS window flags, Windows WDA_EXCLUDEFROMCAPTURE).
• The process does not register a dock icon, menu-bar icon, or taskbar entry.
• The process name on disk is non-descriptive, so a process scan does not surface "Interview Coder."
• The overlay is click-through. It does not steal focus from the assessment window.

These four properties together are why the app does not show up in HackerRank, CodeSignal, CoderPad, Codility, Zoom, Google Meet, or Microsoft Teams screen shares.

How to install and set up

1. Download the Mac (.dmg) or Windows (.exe) build from interviewcoder.co.
2. Install it like any other desktop app.
3. Launch it. It runs in the background. You confirm it's running by the keyboard shortcut, not by a visible window or icon.
4. Sign in. Your subscription credits live on the account.
5. Open whatever assessment platform or video call you're using. Start the screen share if the platform requires one.
6. Trigger the overlay with the global keyboard shortcut. The overlay renders on top of everything on your screen but is invisible to the capture pipeline.

How to use it during a session

Two modes:

Audio mode. The app listens to system audio (interviewer voice through your speakers, headphones, or call audio), transcribes it, and responds. Use this for live interviews where someone is reading you the problem.

Screen mode. The app captures the visible problem statement from your own screen, runs it through the model, and surfaces the solution. Use this for OAs and self-paced assessments where the question is on the page.

The flow during a live coding round:

1. The question is read or shown to you.
2. The app produces a solution outline, the code, and a walkthrough of the approach.
3. You read it,take a moment to analyse it and type it yourself. You do not paste, because paste events are logged and will  get caught.
4. You talk through your reasoning out loud as you implement. To make it seem like you are the one that figured out the solution .

Use cases

• Live coding rounds on HackerRank Live, CoderPad, Zoom-shared editors, Google Meet shared docs.
• Asynchronous OAs on HackerRank, CodeSignal, Codility, and internal platforms.
• System design rounds where you need scaffolding for tradeoffs, capacity estimation, and component breakdown.
• Behavioral rounds where you need a STAR-format response on the fly.
• Take-homes where you want a sanity check on your approach before submitting.

When it does not work

• In-person assessments with a physical proctor in the room. A digital overlay does nothing against a human watching your monitor.

Part 2. How HackerRank tracks you

HackerRank's integrity stack has three layers: proctoring telemetry, structural code analysis (MOSS), and a behavioral ML model that ties them together. 

Browser focus and tab tracking. Every time the assessment tab loses focus (Alt-Tab, Cmd-Tab, clicking another window, exiting full-screen), the event is timestamped and logged. Companies set policies on top of this. Some flag on the first switch, most use a cumulative threshold (typically 3+ switches in a session triggers review). The system also looks for patterns. Regular intervals between switches read as systematic and weight the suspicion score harder than random ones. In Secure Mode, the browser is locked down further: copy-paste blocked, right-click blocked, dev tools blocked.

MOSS (Measure of Software Similarity). Enabled by default on every test. MOSS tokenizes your submitted code, strips out names, whitespace, and comments, and compares the structural fingerprint against a database of past submissions plus public sources (GitHub, Stack Overflow, leaked OA banks). Renaming variables, reordering lines, adding whitespace. None of it works. MOSS sees the AST, not the surface code.

The behavioral ML model.ackerRank moved past MOSS as their primary signal because false positives were too high and AI-generated code wasn't being caught structurally. The current system fuses signals: tab focus events, copy-paste frequency, keystroke dynamics, time-to-solve, and code-iteration patterns. The signs it picks up on:

• Sudden bursts of clean code with no trial-and-error. 
• Unusual pause distributions.
• Lack of incremental debugging.
• Time-to-solve anomalies. Ie. solving a LC Hard in 4 minutes flags or solving a Medium in 90 seconds flags.

HackerRank's current ML model self-reports ~93% accuracy on suspicious-submission detection. But that number is what they publish. Production false positive rates aren't disclosed.

Copy-paste tracking. Every paste event is logged with frequency and (in proctored mode) what was on the clipboard. Pasting your own variable names from a scratchpad still counts as an event.

Image and webcam capture. When proctored mode is on, the webcam takes periodic snapshots, runs face detection for "is the same person here," and looks for second faces, glances off-camera, and missing-face frames.

Session metadata. IPs, geolocation, device fingerprints, browser fingerprints, account history correlation. Multiple candidates from the same IP during overlapping assessment windows is one of the top auto-flags.

Part 3. How CodeSignal tracks you

CodeSignal is more aggressive than HackerRank because their flagship product (Certified Evaluations) requires full proctoring as a feature, not an option.

Mandatory entire-screen recording. When you start a proctored CodeSignal session, you're required to share your entire screen. Not a tab, not a window. Anything that renders on that screen is in the recording: notifications, dock icons, browser tabs you switch to, and any application that draws to your display.

Webcam and microphone for the full session. Both are required. The webcam records continuously, not snapshots. CodeSignal's review team looks for: people walking through frame, candidate looking off-camera in one direction (suggests a second screen), audio of someone speaking answers, audio of typing that doesn't match on-screen typing.

Government ID verification. You upload a photo of a government-issued ID and a selfie. CodeSignal staff verify the match before the result is released.

The Suspicion Score. The CodeSignal-specific signal. It's an aggregated trust score per session, fed by:

• Typing cadence vs the candidate's own warmup baseline
• Mouse movement entropy
• Focus events
• Copy-paste events (CodeSignal records what was copied, not just that copying happened)
• Audio anomalies
• Webcam anomalies
• Code similarity to known solutions

The score determines whether the result auto-verifies or gets pulled into manual review. Manual review is a 1-3 business day process where a CodeSignal proctoring specialist watches the recording end-to-end.

Browser lockdown. CodeSignal's environment can disable copy-paste, block tab switching at the browser level, monitor running processes for screen-share or remote-access indicators (TeamViewer, AnyDesk, Zoom screen-share if it's not theirs), and block browser extensions.

Telemetry from work simulations. CodeSignal's newer assessments use "work simulation" environments that capture more than typing. They measure how you navigate the IDE, how you read the problem, mouse pathing across the spec, and time on each subtask. They compare this to a baseline of candidates working unaided.

Data retention. Recording and ID data is stored for 15 days then deleted. CodeSignal does not share the raw recording with the hiring company. Only a verification result and flag summary.

Part 4. Where the detection has blind spots

1. Anything outside the screen-share API is invisible. Both platforms can only see what your OS reports as part of the captured display. Hardware-layer overlays, OS-level compositor tricks, and processes that opt out of capture (on macOS via specific window flags, on Windows via WDA_EXCLUDEFROMCAPTURE) don't show up in the recording even though you can see them on your monitor.
2. Audio capture is browser-level. They hear your microphone, not your speakers. A second device (phone, tablet) sitting next to you that you read from silently is not picked up by their pipeline. The webcam might catch your eyes glancing. That's the constraint.
3. Behavioral models need a baseline. Without prior keystroke data on you, a first-time candidate's typing pattern only flags on extremes (zero pauses, clean bursts). Pasting code in chunks rather than wholesale, with edits between, stays under threshold most of the time.
4. MOSS needs something to match. Original solutions to original problems generate no MOSS signal. The risk is from public-archive matches, not from your code being "too good."
5. Webcam detection is coarse. It can detect "second face in frame" and "no face for 30 seconds." It does not run gaze-tracking accurate enough to know if you're reading off a second monitor.

Part 5. Practical advice for anyone taking these assessments

• Type incrementally even when you know the answer. Write a stub, run it broken, fix it, run again. The behavioral model cares more about rhythm than code.
• Don't paste even your own snippets from a scratchpad. Every paste event is logged,  instead type it.
• Keep your face centered and your eyes on the screen. Webcam anomalies are the #1 source of manual-review escalations on CodeSignal.
• Stay in full-screen. Cmd-Tab and Alt-Tab leave timestamps. If you need to look something up that the assessment allows, do it through the assessment's own browser instance.
• Talk through your thinking out loud, even on solo OAs. Audio of you reasoning is the strongest signal for you in a manual review.
• Run your tests visibly. Use the platform's built-in test runner. Manual print statements and test invocations are evidence of real work.
• Close every non-essential process. Process scans flag more than you'd think (Discord overlay, Nvidia overlay, screen-recording software you forgot was running).
• Match your warmup typing speed to your assessment typing speed. A candidate who's 40 wpm in warmup and 110 wpm during the test gets flagged.

Part 6. Why it was built and what's in the product

Every mechanism in Parts 2 and 3 has a shape, and that shape can be addressed at the OS layer instead of the application layer. The browser-based defenses (focus events, screen-share API, mic hooks, copy-paste interception) only see what the browser sees. A native application that opts out of display capture, runs without an icon, captures audio through an OS-level pipeline, and stays click-through is outside that detection surface by design.

That is the entire reason InterviewCoder exists. It is a native desktop binary written against the OS APIs that control display capture and audio routing.

What's in the product:

• Audio mode and screen mode (covered in Part 1)
• Coding assistance covering algorithms, system design, behavioral, full-stack, ML, data, trading, product, and consulting interviews
• Coverage for HackerRank, CodeSignal, CoderPad, Codility, Zoom, Google Meet, Microsoft Teams, Webex, Chime, Lark
• macOS (Apple Silicon) and Windows builds
• Daily detection testing against the major platforms, with a status indicator on the site

Plans:

• Free tier: download the app, explore the interface, basic features.
• Monthly Pro: $299/month. 1,000 monthly credits, full model access, 24/7 support.
• Lifetime Pro: $799 one-time. Unlimited lifetime access.

The pricing is higher than most prep tools because the cost structure is different. Standard prep tools charge $20-50/month because they ship a question bank and a video player. InterviewCoder ships a native binary that has to keep up with OS updates, capture-API changes, and platform-side detection updates on macOS and Windows. The team is small and the testing surface is large. The price reflects what it costs to keep the bypass working in 2026.

If you have questions about specific platforms (CoderPad, Codility, HireVue, ByteBoard), drop them in the comments. We'll keep this post updated as detection methods evolve.

cant they see your cheating by looking at your eyes and not explaining the code? itll be a hard system design with coding they said so not leetcode, how do i explain the code ai generates if i dont understand it lol, also using the open source version of this software

I’m currently in my 2nd year and preparing for placements in August, and honestly starting to feel a bit stressed about my consistency.

I had taken TUF+ last December thinking I will stay disciplined, but till now I’ve only solved around 51 problems. A big reason is that the last few months went by in academics, tech fest work, and some personal issues, so I couldn’t stay regular.

The main issue is consistency. I start strong for a few days, then break the streak and it gets hard to get back on track.

For those who have been through this phase:

• How did you stay consistent daily?
• Did you follow any specific plan or routine?
• How do you deal with days when you just don’t feel like solving anything?

Would really appreciate any honest advice or strategies that worked for you.

Thanks in advance :)

I got the Amazon OA link and tried the sample test using the Interview coder free version. Now in the repo coding round, shortcuts like Ctrl + G are opening few editor windows in the IDE. I was thinking of buying the premium version. But now seeing these shortcuts getting recorded, I am scared now that I may be flagged for cheating

OpenAI platform SWE, five rounds, two and a half weeks. They are hiring fast right now, recruiter mentioned headcount is roughly doubling by end of year, and with GPT 5.5 just shipping the infra teams are pulling people in as fast as they can.

Phone Screen

90 minutes split between coding and a mini system design, which caught me off guard. Coding was a real time event aggregator, given a stream of events with timestamps, maintain rolling counts over 1 min, 5 min, and 1 hour windows. Went with a deque per window, interviewer immediately asked me to handle out of order events which broke my approach. Switched to a sorted bucket structure and got it working with maybe 8 minutes left.

System design portion was design a webhook delivery platform with retries, dead letter queue, and per tenant rate limiting. Only had 30 minutes for it and the interviewer kept layering constraints. What if a tenant has a sustained burst, what if their endpoint dies for an hour, what if they need delivery ordering. Did not finish cleanly, walked out thinking I bombed.

Take Home

48 hour window to build an in process queue with at least once delivery, visibility timeout, and a basic admin API. The instructions said clean code matters more than feature completeness so I took it seriously. Built it in Python with SQLite, wrote a real test suite, included a readme that walked through every tradeoff.

The visibility timeout was the catch. Worker grabs a job and crashes, job needs to come back eventually but not too soon, and you have to handle the case where a worker finishes after the timeout has expired and you have already redelivered. Ended up with a lease token approach where the worker only commits if its token is still valid. Took me about 7 hours total.

Coding Round 2

Token level streaming. Given an LLM that produces tokens with timestamps, build a streaming text differ that shows what was added, modified, or deleted as the stream evolves, with the ability to roll back to any previous state. Niche, but this is literally the kind of thing they need internally for assistant message editing.

Used a versioned tree structure where each token maintains a chain of versions and the differ walks the chain. Interviewer kept pushing edge cases, two tokens swapping positions, the stream getting interrupted mid token. Got through most but my rollback had an O(n) op I could not get rid of in time.

System Design

This was the round. Design ChatGPT.

Yes, that question, asked at the company that built it. So they go deep. Started with the obvious pieces, request routing, model serving, conversation persistence, but the interviewer was not interested in any of that. He wanted to talk about scheduling. How do you allocate GPU capacity across free, plus, pro, and api tiers when traffic spikes are correlated. How do you bias the scheduler toward keeping pro users happy without starving free tier. How do you handle a single conversation that spans multiple model versions because the user kept it open across a deployment boundary.

Spent the last 20 minutes on one question. How would you autoscale the serving fleet when GPT 5.5 has a different latency profile from 4o, given that the same scaling signals give you wrong answers across models. I argued queue depth weighted by estimated output token count, which decouples the scaling decision from the model under it. Interviewer did not say if I was right but he stopped pushing back, which I took as a small win.

Hiring Manager

45 min, infra lead. Past projects, debugging philosophy, scaling stories. He described two real tradeoffs the team is wrestling with and asked which I would pick. Both were latency vs cost and I went higher cost both times, because you can always optimize later but you cannot unship a slow product. He liked the framing.

Got the offer four days later.

A few takeaways. The system design rounds at OpenAI are not generic, they want to know if you can reason about their actual problem space, GPU scheduling, multi tenancy, model serving, autoscaling under non stationary traffic. Read up on inference serving (vLLM, TensorRT, continuous batching) before you go in. The take home is treated as a writing sample, not a code sample. Spend half your time on the readme and the tests. The cognitive flexibility piece is real, they throw new constraints mid round and you have to absorb them without losing the thread, practice that specifically.

If you have a loop coming up, GPT 5.5 just shipped which means the platform team is in chaos for a while. Now is a good time.

I have an interview coming up role "NVIDIA Interview for Software Platform Support Engineer (DGX Cloud)". What can I expect in interviews.

Would love to know:

- Interview process difficulty?
- Focus areas (DSA vs system design vs AI)?
- Work culture / stability?

I have ~2 YOE in AI + backend (RAG, FastAPI, real-time systems).

Any insights would help 🙏

[ Removed by Reddit on account of violating the content policy. ]

[ Removed by Reddit on account of violating the content policy. ]

Just started scheduling interviews. I’ve been a swe for 5 years and am technical lead of my team. I’m full stack. But when I’m looking at these invites for interviews (mid-late stage private AI companies), they have implementation rounds which scare me.

Maybe I’m just worried because I’m afraid of the realization that I wasn’t doing much impactful work at my job (mostly CRUD). Leetcode interviews seem easier to manage - at least I know what I’m signing up for. With these implementation interviews, the problem space seems infinite.

Did others feel this way before starting interview prep?

Signed the offer last week. amazon sde2, 210k total comp year one. I used interview coder for every coding round. This sub helped me figure it out so here is the full playbook.

First thing, and I cannot stress this enough, the tool does not work if you have no base knowledge. If you walk into an amazon loop with zero prep and an overlay, the interviewer will ask you to explain your approach, you will fumble, and it is over. The overlay writes code, it does not talk for you.

For context on where I am coming from, CS grad from a state school, nothing ivy, learned python sophomore year through free youtube courses and codecademy before going full time on leetcode senior year. One internship at a non name brand company. I had the fundamentals but I was nowhere near a top tier candidate on paper, which is exactly why I needed a tool.

the 2 week prep

LeetCode, 30 mediums and 5 hards from the amazon tagged list on blind75 and neetcode, 3 a day for 14 days, solving for pattern recognition not memorization.

Pattern recognition is the whole game, if you can identify bfs, dfs, two pointer, sliding window or dp in 30 seconds, you can talk about the problem intelligently while the overlay writes the code.

Leadership principles, 8 STAR stories, 2 per round, amazon drills on these and interview coder cannot save you here, you need to memorize them.

Light system design for the bar raiser round, grokking plus one youtube playlist was enough for sde2.

setup

You do not need a second laptop, the overlay is invisible to screen share even on your main display. a mod broke down the full setup in this comment, go through it before your loop.

Run a mock with a friend on zoom before the real interview, position the overlay close to your camera so your eyes do not obviously drift sideways mid problem.

during the round

Read the problem out loud, clarify edge cases, narrate your thinking, this buys you 30 seconds to actually process while looking like a strong candidate.

State your approach before you look at the overlay, if it agrees go, if it diverges stop and think about why, sometimes the overlay picks the cleverer solution when the interviewer wants the obvious brute force first.

Narrate as you code, "starting with a brute force then optimizing", this is what strong candidates do naturally and it keeps you from freezing while the overlay catches up.

When they push on an edge case, do not paste the fix, talk through it and then adjust, the interviewer is testing whether you understand why, not just that you can type.

what it won't do

It won't help you on behavioral rounds, those are yours, memorize your STAR stories.

It won't help you in verbal design debates, the interviewer will argue tradeoffs and the overlay cannot argue back.

It won't save you on theory follow ups, if they ask why hashmap instead of sorted set and you cannot answer, they know.

tldr, 2 weeks of real prep plus interview coder is the combo, neither works alone, 210k on the other side.

I have an oa coming up and it’s fully proctored. However, the company insists that I should take the hackerank challenge from the hackerank desktop app - none of the companies I have ever applied to demanded this before, I’ve been doing the hackerrank challenges from the web directly. Is the interview coder tool undetectable to this desktop app version. I would be happy if one of the mods guided me on this.