If you are actively applying, you should take a look at this.

Throwaway-ish because if recruiters look at my main account they’ll probably ding me for "lack of cultural alignment" or whatever corporate astrology they use these days.

I need to get this off my chest because I genuinely don't understand what hiring managers want in 2026. Every job board—LinkedIn, Unstop, careers portals, cold email threads—feels like a dead-letter office run by a broken ATS script. Hundreds of tailored applications, customized cover letters, and zero human responses. Just automated rejections dropping at 3:14 AM.

I am not applying with a generic portfolio full of weathered MERN-stack todo apps or boilerplate weather dashboards. I’ve spent the last few years grinding through systems, edge compute, and embodied AI:

• Embodied AI & Robotics: Deep into the ROS 2 ecosystem right now, working on Vision-Language-Action (VLA) pipelines for robotic manipulation—not just calling wrapper APIs, but dealing with action chunking, flow matching, and hardware latency.
• Published Edge AI Research: Co-authored and published peer-reviewed IEEE research on real-time vision-language assistive systems, specifically dealing with multimodal fine-tuning optimized for low-power edge compute.
• Model Fine-Tuning & Weights: Hands-on experience fine-tuning VLMs, LLMs, and Machine Translation (MT) models with LoRA/PEFT, alongside quantization workflows to make them actually run without melting small hardware.
• Embedded & Edge Silicon: Spent countless hours with logic analyzers, serial debuggers, and pinouts across ESP32, Arduino, Raspberry Pi, Jetson Nano, and Jetson Orin Nano, running accelerated inference pipelines on actual edge silicon.
• Low-Level Systems Tinkering: Messed around writing a toy OS for the Raspberry Pi Zero 2W. Leaned on AI for parts of the codebase, debugging, and ideation, but did plenty of the heavy lifting myself—it boots on hardware, and in emulation it runs a working filesystem, GUI, and image renderer (though the physical SD card driver humbled me before I could get bare-metal storage working).
• Prior Experience: Already wrapped up 2 actual internships doing production engineering work.

And despite all of that? Ghost towns. Complete silence.

I open job postings labeled "Junior / Internship" and the requirements read like a 10-year staff architect checklist, only for the role to either get canceled after three weeks or filled internally by a referral before the listing even went live.

I’m completely done doom-scrolling "Easy Apply" queues and drafting personalized cold emails to founders that bounce into spam folders. If anyone here is building real stuff—robotics stacks, low-level systems, edge ML, or embedded compute—and actually values someone who gets their hands dirty across the entire stack, my DMs are open.

Otherwise, someone please tell me what exact incantation I need to put on a resume to get past a resume parser in this economy.

I’m sure this sub is overflowing with posts like these, but I figure I should ask

All I manage to get is contract jobs

I have no degree, tons of projects and half decent contributions but none of it seems like it’s enough. I know how my lack of a degree cripples me here, among other things.

But I was always told it’s possible regardless. Am I making a mistake here?

I’m a 2026 B.Tech graduate looking for Software Developer / SDE opportunities.

Hi everyone. What can i expect in the Ebay’s first interview with the staff engineer (technical screen and soft skills) for the position of staff frontend engineer and they have also provided a link for code signal. The interview is tomorrow and i have no idea what to expect. Any help or guidance shall be appreciated
Thanks in advance

Hi everyone! 🙏

I'm a 2026 Computer Science Engineering graduate currently based in Pune, actively looking for a full-time opportunity. I'm hoping to secure a job and join before Diwali, ideally before 1 November 2026.

My first preference is AI engineering, especially roles involving LLMs, RAG, and AI agents. I'm also open to backend engineering and MERN stack development opportunities.

🤖 Preferred Roles:

\- AI Engineer / GenAI Engineer

\- LLM Engineer / AI Agent Engineer

\- Backend Software Engineer

\- MERN Stack / Full Stack Developer

💻 Technical Skills

AI/GenAI: Python, LangChain, LangGraph, LangSmith, RAG, LLMs, AI Agents, Tool Calling, Pydantic, Embeddings, Vector Databases, Ollama, OpenAI, OpenRouter.

Backend/Full Stack: JavaScript, TypeScript, Node.js, Express.js, React.js, Next.js, REST APIs, MongoDB, PostgreSQL, Redis, JWT, RBAC, Docker, Git, CI/CD.

I've built projects involving AI-powered data analysis, autonomous task agents, LLM workflows, and full-stack web applications. I also have hands-on freelance experience developing and deploying web applications.

📍 Current Location: Pune

🚀 Open to Relocation: Bangalore and Hyderabad

💼 Experience: Fresher with practical project and freelance experience

⏳ Availability: Immediate / short notice

🎯 Target: Join before 1 November 2026, if possible.

I'm genuinely looking for an opportunity to begin my professional career before Diwali. If your company is hiring, you know a recruiter or hiring manager, or you can refer me for a suitable role, I'd be extremely grateful.

I'm happy to share my resume and attend interviews at short notice.

Even a referral, a recruiter contact, or a lead on a startup hiring for these skills would mean a lot to me.

Thank you for reading and for any help you can offer. ❤️

I cleared Google’s interview process, and my packet was approved by the Hiring Committee. I’ve been waiting for a team match for about six weeks, but haven’t received any team-match calls yet. My background is mainly frontend and frontend-heavy full-stack.

For people who’ve gone through team matching in India recently: is a wait like this common, or has hiring slowed down? How long did it take you to get your first team conversation?

I cleared Google’s interview process, and my packet was approved by the Hiring Committee. I’ve been waiting for a team match for about six weeks, but haven’t received any team-match calls yet. My background is mainly frontend and frontend-heavy full-stack.

For people who’ve gone through team matching in India recently: is a wait like this common, or has hiring slowed down? How long did it take you to get your first team conversation?

I filled out an application over the weekend and came to a required question about my desired salary. The company explicitly said not to write that the salary was negotiable, even though the job posting did not include any pay range at all. I entered $150,000 and added a short note saying that the process felt unfair because if I guessed too high, I could be rejected, and if I guessed too low, I could lose money.

I expected that the note had probably ruined my chances. The next morning, a recruiter sent me a rejection because my expectations did not align with the compensation range they had established from the start. The message also said they wanted to be transparent so that no one would accept the job and later feel underpaid.

Transparent about what, exactly? They clearly had a range from the beginning, and they were the ones who chose not to disclose it.

Since my application had already been rejected, I replied and told them that their email proved the exact problem I had been talking about. I did not need $150,000 and would have been happy to discuss the actual compensation package, but they forced me to guess a number before speaking with anyone. The job seemed interesting and well suited to my experience, yet a single number I entered without any information eliminated me from consideration. I really cannot stand this part of applying for jobs.

Mitre Media is hiring a remote Tech Lead Full-Stack Rails Engineer. Category: Software Development 💸Salary: $170k - $200k 📍Location: Remote (USA, Canada, USA timezones)

See more and apply here!

Hi everyone,
Im sorry for my english in advance, im not a native speaker.
I wanted to ask people working as software engineers if with my university profile I still have a chance in the field.
I’ve started university this October with my profile being: databases, business analysis and data science.
I’d really like to work in software engineering and my profile is the closest Ive found to that. In my country there was basically only cybersecurity, computer science, game development, graphic design to choose from.
So my question is wether I can finish that profile and be able to work in software engineering with additional skills I’d learn privately or is it just a big mistake. :)

Received a Google Hiring Assessment for an Senior SWE role. What type of questions and difficulty should I expect, and how should I prepare to maximize my chances of clearing it? Any tips from recent candidates would be appreciated!

​

Which one is the best from your perspective? For me, those options are somehow resistant in this AI context.

We are a small technical team focused on GPU kernels and production inference systems. We help AI startups, inference API teams, GPU providers, and companies running self-hosted models make inference faster, more reliable, and less expensive.

If your team is responding to slow generation or high GPU bills by adding more hardware, we can first determine whether the real constraint is kernels, memory movement, quantization, KV-cache layout, batching, scheduling, model loading, or the serving architecture.

We can help with:

- CUDA and Triton kernel development, fusion, profiling, and benchmarking

- PyTorch model-path optimization and numerical validation

- vLLM, SGLang, TensorRT-LLM, and custom serving systems

- quantization, VRAM reduction, KV-cache layout, continuous batching, and CUDA Graphs

- time to first token, inter-token latency, throughput, and tail-latency analysis

- scale-to-zero, cold-start behavior, readiness, cancellation, streaming, and failure recovery

- GPU utilization, cost-per-request analysis, and capacity planning

- reproducible before/after benchmarks with correctness gates and documented limitations

A typical engagement starts with a focused inference audit:

1. Define the model, framework, GPU, traffic shape, latency or throughput target, and cost constraint.

2. Profile the current path and identify the dominant bottlenecks.

3. Agree on the smallest changes worth implementing.

4. Deliver code, before/after measurements, correctness checks, and remaining risks.

5. Help integrate and validate the optimized path under realistic load.

Good fits include a slow or expensive inference endpoint, VRAM pressure preventing the desired batch size or context length, unstable serving under load, poor GPU utilization, costly cold starts, or a custom kernel/model path that needs profiling and validation.

For an initial conversation, send a Reddit chat or private message with:

- model and framework

- GPU type and VRAM

- current latency, throughput, utilization, or cost problem

- target workload and success metric

- deployment environment

- timeline and approximate budget range

We will tell you directly whether the workload appears optimizable and what evidence would be needed before making a performance or savings claim.

The best engineers are burning out... and it’s not because of the workload.