Hello All,

I was hoping to get some insight in how people who were in the EHS field got into FAANG companies? I haven’t had any luck thus far, besides a phone screening with Apple, and interview ended up getting canceled because they had an internal candidate further in the process. I have about 6 years of experience in EHS, mostly in government roles. Majority in lab based environments, with this year being a new role in industrial hygiene.

Obtaining my Associate safety professional certificate is high on my priority list which could be a big help. But wondering if anyone has any other tips that could help as I feel like I’ve been hitting a wall on applications.

May sound silly to say since all FAANG companies are so difficult to get into, but I’m eyeing Apple and Google for what I think are more safe in terms of job security as well as healthier work environments (but I know that depends on team you are in).

Covering roughly 24–31 Aug. Pulled from Top Jobs Today daily alerts and the live board. Skipped the intern flood. None of these listings had a real advertised range on the job page, so I’m not making pay up.

Week pulse: 555 jobs from 22. Google led volume (92). GitLab dumped 83 in a single day. OpenAI had a one-day burst (25). Microsoft 56, Amazon 41, Stripe 32.

Roles that stood out

1. OpenAI — Product Engineer, Full Stack - Agents — San Francisco
   https://topjobstoday.com/company

2. Nvidia — Senior Machine Learning Engineer, AI Safety — 2 locations
   https://topjobstoday.com/company/nvidia/job/senior-machine-learning-engineer-ai-safety-2-locations

3. Google — Product Manager, AI Google Search Growth — Mountain View, CA
   https://topjobstoday.com/company/google/job/product-manager-ai-google-search-growth-place-mountain-view-ca-usa

4. Booking — Senior Machine Learning Scientist - Agentic Modelling Team — Amsterdam
   https://topjobstoday.com/company/booking/job/senior-machine-learning-scientist-agentic-modelling-team-netherlands

5. Reddit — Staff Product Manager, Ads Formats — Remote, United States
   https://topjobstoday.com/company/reddit/job/staff-product-manager-ads-formats-remote-united-states

6. Spotify — Senior Product Designer, Platform and Partner Experience — London or Stockholm
   https://topjobstoday.com/company/spotify/job/senior-product-designer-platform-partner-experience-london-or-stockholm /openai/job/product-engineer-full-stack-agents-san-francisco

7. OpenAI — Product Manager, Multimodal Safety — San Francisco
   https://topjobstoday.com/company/openai/job/product-manager-multimodal-safety-san-francisco

8. Anthropic — Manager, Forward Deployed Engineering — New York City, NY
   https://topjobstoday.com/company/anthropic/job/manager-forward-deployed-engineering-new-york-city-ny

9. Anthropic — Product Manager, Claude Science — San Francisco / NYC / Seattle
   https://topjobstoday.com/company/anthropic/job/product-manager-claude-science-san-francisco-ca-new-york-city-ny-seattle-wa

10. GitLab — Staff Forward Deployed Engineer — Remote, US
    https://topjobstoday.com/company/gitlab/job/staff-forward-deployed-engineer-remote-us-2

11. Replit — AI Agent Security Architect — Foster City, CA
    https://topjobstoday.com/company/replit/job/ai-agent-security-architect-foster-city-ca

12. Uber — Staff Machine Learning Engineer - AV Labs — Sunnyvale, CA
    https://topjobstoday.com/company/uber/job/staff-machine-learning-engineer-av-labs-sunnyvale-ca-united-states-1

Hi everyone!

I’m a recent M.S. in Computer Science graduate and I’m currently looking for early-career Software Development Engineer (SDE) opportunities at Amazon in the U.S.

I came across an Amazon SDE job posting that I’m very interested in and would really appreciate a referral from an Amazon employee, preferably someone at the L5/L6 level who is familiar with the SDE hiring process.

A little about me:
• M.S. in Computer Science, Arizona State University
• Software development internship experience
• Experience with Java, C++, Python, Spring Boot, React, AWS, Docker, Kubernetes, SQL, and distributed/data-processing systems
• Built projects involving AWS, real-time data pipelines, ML/LLM applications, and backend systems
• Currently targeting early-career/new-grad SDE roles in the U.S.

I’d be happy to send my resume and any additional information needed to determine whether I’d be a good fit before submitting a referral.

If you’re able to refer or can point me toward the right person, I’d really appreciate it. Thank you!

Gone are the days when senior and above can easily get 500-800K package, companies are now offering 150k for seniors without feeling shameful, take it or leave it attitude. What used to be internship stipend salary or admin/hr lowly paid workers' salary are now being offered to Data Scientists, Project Managers and even Senior SWE! Welcome to new era of depressing Tech career!

Tech Interview Questions

Hi everyone!

I’m a recent M.S. in Computer Science graduate and I’m currently looking for early-career Software Development Engineer (SDE) opportunities at Amazon in the U.S.

I came across an Amazon SDE job posting that I’m very interested in and would really appreciate a referral from an Amazon employee, preferably someone at the L5/L6 level who is familiar with the SDE hiring process.

A little about me:
• M.S. in Computer Science, Arizona State University
• Software development internship experience
• Experience with Java, C++, Python, Spring Boot, React, AWS, Docker, Kubernetes, SQL, and distributed/data-processing systems
• Built projects involving AWS, real-time data pipelines, ML/LLM applications, and backend systems
• Currently targeting early-career/new-grad SDE roles in the U.S.

I’d be happy to send my resume and any additional information needed to determine whether I’d be a good fit before submitting a referral.

If you’re able to refer or can point me toward the right person, I’d really appreciate it. Thank you!

Hey everyone!

I recently got an email inviting me to the technical interviews for the Google SWE Summer Internship 2027. I have around 10 days left to prepare.

Could anyone who has been through the Google interview process please suggest what topics I should focus on the most?

I am currently focusing heavily on Graphs and Trees — are standard problems from these topics enough, or should I cover more? Should I focus on DP too?

Any tips or suggestions would be really helpful. Thanks in advance!

I'm 26 and from Morocco and I'm trying to figure out how realistic it is for me to get a career in tech.

I have a bachelor's degree, but it's in biology, not CS. I started learning programming around 2020 and have done some freelance web stuff and worked professionally as a developer recently in 2025 for 1 year, mostly with TypeScript/Next.js/Postgres. My experience was at a small startup working on ai health.

The problem is my overall career history is pretty messy. I have gaps, and I'm currently unemployed. So on paper I don't look like someone who followed the normal CS degree -> internship -> junior -> mid-level path at all.

Right now I'm trying to decide whether going hard on backend/cloud/infrastructure is actually a realistic way out of this.

I'm studying for AWS SAA and working on getting better at Linux, Docker, networking, databases, backend/system design, etc. I'm also considering eventually doing stuff like LFCS/CKA and building a couple of serious projects that actually demonstrate these skills.

I'm willing to put in several focused hours every day for the next 6-12 months. I'm not expecting to magically become a cloud engineer because I got an AWS cert.

What I'm worried about is whether I'm trying to fix a profile that recruiters will just filter out anyway because of the gaps + unrelated degree + limited professional experience.

If you were in my position, what would you actually focus on?

Would you target backend developer jobs first and move toward cloud/platform later? IT/support/sysadmin -> cloud? DevOps junior roles? Something else?

And especially for anyone involved in hiring: if this profile landed in front of you, what would stop you from interviewing me, and what 2-3 things could I realistically change over the next 6 months that would make the biggest difference?

I'm fine with harsh answers. I'm mainly trying to avoid spending another year learning a ton of stuff that doesn't actually make me more employable.

So I have recieved a verbal offer from Microsoft and currently I work in an SBC(recently got into a new project). What should I do? My joining is already been communicated as 19th october.

1. Wait for the written OL and then resign
2. Discuss with my current manager beforehand that I might not be staying etc.
3. Any other options?

Ps - this is my first switch so kinda worried abt my notice period and all as well.

This morning, I opened my inbox and found out that my role had been eliminated.

There had been some reorg activity, but no real warning from my manager and no indication that my team was at risk. Yesterday, I was planning next week’s work. Today, my time at Amazon is over.

The hardest part is how sudden and impersonal it feels. You can spend years trying to perform well, deliver results, and do everything “right,” only to realize that none of it guarantees you’ll still have a badge tomorrow.

I’m trying to focus on what I can control now: updating my resume, reconnecting with people, and preparing for interviews. I’ve also been using PracHub to organize interview prep and practice common technical and behavioral questions:

For anyone else going through this: how are you handling the transition?

Salesforce

• OA - 1 medium + 1 Hard bitmasking question
• Screening(1.5 hrs) - 2 medium DSA questions(30 mins) + 1 LLD question(1 hr)
• R1 onsite(1.5 hrs) - 1 DSA question + LLD(Connect 4 problem). shortest path from node n to all nodes will be marked as x(blue color). Return all possible paths from 1 to n such that x for that node is less than previous node. (2 times Djikstra in 1 problem).
• R2 onsite(1.5 hrs) - Design HLD of notification service.(NotificationPriority-High/low, Template, UserPreferences, User-DND). Scalability, Resilience, Availability. Had discussion in depth on kafka.
• R3 HM(1.25 hour) - Biggest accomplishment? Major project? Conflict with teammate/manager? Any failed project? How to do hashing of String? System design of messaging system? System design of temporal(10 mins) as i have worked on temporal.

Salesforce

• R1(1 hr) - 2 DSA problem - medium level leetcode
• R2(1 hr) - LLD of JobScheduler. use delay instead of polling.(Rejected)

Oracle Cloud

• DSA - question on next greater element.
• HLD - MessageScheduler(similar to Jobscheduler). At scheduled time, message can be delivered to 1-10k users. delivery should with 1 sec of scheduled time.
• Bar raiser - Asked question on project + 2-3 standard behavioural question.
• HM round - Project + Networking questions. How ping works? TCP vs UDP? Few more tough networking/linux problems.

Oracle Health(SSE + AI role)

• Screening - 2 medium DSA questions.
• HLD - Rate Limiter(no followup asked by interviewer) - simple interview.
• DSA - Asked 1 question
  • Place k buildings on n * m matrix. distance from every empty location to closest building is x. Building should be placed in such a way that maximum of all x should be minimum.(Expected: brute force as n, m <=8).
• Bar raiser round: Asked question on project + 2-3 standard behavioural question.
• HM round: Project discussion(20 mins). Created any MCP tools?(rejected).

Walmart

• HLD (asked in 2 different roles over period of 1 year)
  • Google docs
  • Create system for checking Malicious content on blob storage(AWS S3, Azure and so on.),
  • Rate Limiter for Big Billion sale.
• LLD + Java - Naukari Portal(30 mins). Focus on Person, Job u/ManyToMany relationship. Various questions on Java, K8s, Docker, SpringBoot.
• DSA - basic medium level (top 75 questions)
• Overall: Should know the Java+SpringBoot thoroughly. DSA medium is enough. LLD + HLD(top 10 is enough).

Microsoft Azure

• 2 OA questions
• R1 - DSA(30 mins - Rotten orange) + OS concepts + few questions on projects.
• R2 - DSA(40 mins - Basic tree question + long question to read) + OS concepts(paging, virtual memory, etc)
• R3 - HLD(1.5 hrs) - OTP service(multi tenant) + Notification service.
• AA(1.5 hrs) - 3 questions on how physical + virtual adressing on different devices(RAM, SSD, Hard disk)
• AA(2nd team - 30 mins) - Web crawler(System Design) + inverted indexing + 2 behavioural question. Why Hyderabad relocation? Any conflicts with you manager?

Flipkart

• Machine coding - 90 mins - Food delivery service. All functionalities(place/cancel/pickup order + few more) + Good to have(rating + delivery partner ranking). Expectation: OOPs concepts + extensible + proper format.
• 2 medium DSA - topological sorting + 1 more
• HLD - Rate Limiter(Deep discussion)
• HLD expectation: should know the Rate Limiter and should be able to explain each component at deep level.
• My view: Should know the basic questions of DSA. top 10 LLD + HLD questions(need deep understanding).

Tekion

• DSA: medium level questions
• LLD: Workflow orchestrator

Prep Resource: LeetCode & PracHub Company tagged questions

Hi

Does anyone struck after giving screening for Meta E4 SWE, ML and before virtual on-site interviews? I gave my screening on May 18th and then couldn’t schedule my virtual on-site after my screening round. Does
anyone know when would they begin hiring again ?

Thinking about taking a pay cut for better work-life balance. Which companies are actually worth looking at?

I got screening call right after 2 days of my application, and later the interview was scheduled for 25th Aug. I wanted to know the timeline usually how long Microsoft takes to give results. Things moved pretty fast initially though but still kinda curious to know.

Anyone recently completed 3 round interviews for Microsoft IC2 Software Engineer IDNA 200043430 USA? Got any feedback or offers from Microsoft?

I recently encountered this Amazon grid problem and wanted to share the approach.

Approximate date: August 20, 2026
Expected solving time: Approximately 40 minutes
Topics: Multi-source BFS, binary search, geometry

Problem

A city is represented by an n x m grid:

• 1 represents an existing delivery center.
• 0 represents an empty cell.

The distance between two cells is the Chebyshev distance:

distance((x1, y1), (x2, y2))
    = max(abs(x1 - x2), abs(y1 - y2))

The inconvenience of the grid is the maximum distance from any empty cell to its nearest delivery center.

You may convert at most one 0 into 1.

Return the minimum possible inconvenience after adding the new delivery center.

Example

n = 2, m = 4

grid =
0 0 0 1
0 0 0 1

Adding a delivery center at (0, 0) gives:

1 0 0 1
0 0 0 1

Every remaining cell is within distance 1 of a delivery center, so the answer is:

1

Observation 1: Multi-Source BFS

Chebyshev distance corresponds to moving in eight directions:

up, down, left, right, and the four diagonals

Start a BFS simultaneously from every existing delivery center. This calculates:

dist[r][c] = distance to the nearest existing center

in O(nm) time.

Observation 2: Binary Search the Answer

Suppose we want to determine whether inconvenience D is achievable.

Every cell with:

dist[r][c] <= D

is already covered by an existing delivery center.

Only cells satisfying:

dist[r][c] > D

must be covered by the new center.

If inconvenience D is achievable, every larger value is also achievable. This monotonic property allows binary search.

Feasibility Check

For a bad cell (r, c), the new center (x, y) must satisfy:

max(abs(x - r), abs(y - c)) <= D

This is equivalent to:

r - D <= x <= r + D
c - D <= y <= c + D

Therefore, each bad cell creates an axis-aligned square containing every valid location for the new center.

We intersect these ranges across all bad cells:

rowLow  = max(rowLow,  r - D)
rowHigh = min(rowHigh, r + D)

colLow  = max(colLow,  c - D)
colHigh = min(colHigh, c + D)

The candidate inconvenience is feasible when:

rowLow <= rowHigh
and
colLow <= colHigh

If there are no bad cells, no additional center is required.

C++ Solution

#include <algorithm>
#include <queue>
#include <utility>
#include <vector>
using namespace std;

int minimumInconvenience(vector<vector<int>>& grid) {
    int n = grid.size();
    int m = grid[0].size();

    const int INF = 1e9;
    vector<vector<int>> dist(n, vector<int>(m, INF));
    queue<pair<int, int>> q;

    for (int r = 0; r < n; ++r) {
        for (int c = 0; c < m; ++c) {
            if (grid[r][c] == 1) {
                dist[r][c] = 0;
                q.push({r, c});
            }
        }
    }

    const int directions[8][2] = {
        {-1, -1}, {-1, 0}, {-1, 1},
        {0, -1},           {0, 1},
        {1, -1},  {1, 0},  {1, 1}
    };

    while (!q.empty()) {
        auto [r, c] = q.front();
        q.pop();

        for (const auto& direction : directions) {
            int nr = r + direction[0];
            int nc = c + direction[1];

            if (nr < 0 || nr >= n || nc < 0 || nc >= m) {
                continue;
            }

            if (dist[nr][nc] > dist[r][c] + 1) {
                dist[nr][nc] = dist[r][c] + 1;
                q.push({nr, nc});
            }
        }
    }

    auto feasible = [&](int limit) {
        int rowLow = 0;
        int rowHigh = n - 1;
        int colLow = 0;
        int colHigh = m - 1;

        for (int r = 0; r < n; ++r) {
            for (int c = 0; c < m; ++c) {
                if (dist[r][c] <= limit) {
                    continue;
                }

                rowLow = max(rowLow, r - limit);
                rowHigh = min(rowHigh, r + limit);
                colLow = max(colLow, c - limit);
                colHigh = min(colHigh, c + limit);
            }
        }

        return rowLow <= rowHigh && colLow <= colHigh;
    };

    int low = 0;
    int high = max(n - 1, m - 1);

    while (low < high) {
        int middle = low + (high - low) / 2;

        if (feasible(middle)) {
            high = middle;
        } else {
            low = middle + 1;
        }
    }

    return low;
}

Complexity

Multi-source BFS: O(nm)
Each feasibility check: O(nm)
Binary-search iterations: O(log(max(n, m)))

Overall:

Time:  O(nm log(max(n, m)))
Space: O(nm)

The useful insight is that Chebyshev-distance balls are ordinary axis-aligned squares. This makes the feasibility check much simpler than the coordinate transformation commonly used for Manhattan-distance problems.

By any chance someone knows what are the next steps after the Criteria OA?

Any help would be appreciated!