I’m an AI research engineer based in Africa with about 3 YoEs in RL, LLMs, post-training, and systems efficiency (CUDA/vLLM). However, on a smaller scale because of compute limits. My undergrad and Master’s are both in communications engineering. I did a 5th-year Master's at my local university while working full-time at a telecom company, focusing on RL in telecom. Publication-wise, I have one paper published in a peer-reviewed mid-tier AI/telecom conference, a patent, and another paper that was accepted but never published because we couldn't fund it.

Over the past year, I’ve hit the below walls:

PhD Applications (~10 mid-tier labs): Rejected by all. I read their recent papers, put together proposal ideas, and reached out to PIs, but didn't get traction.

Research Engineer Roles (~100 applications): Applied directly, reached out to engineers in similar roles for resume reviews or referrals, but didn't pass a single initial screening.

Cold Emails for Internships (~400 sent): I read labs' papers, asked targeted questions, and chatted with authors. They’d often be interested in the technical ideas, but as soon as I brought up doing an internship, I almost always got ghosted (got 3 actual replies, all saying no capacity).

I’m starting to think the location and university brand are major bottlenecks, so I'm considering doing a second CS Master’s abroad to get my foot in the door. But I really don't want to spend two years going in the wrong direction if there's a better way.

What other paths or changes would you suggest?

I am applying for a PhD this cycle and am interested in a research lab focused on LLMs, Reinforcement Learning, and Computer Vision. While my research interests align with CS, the lab is affiliated with the ECE faculty.

Could you please clarify:

• Should I submit my application through the ECE or CS department if I prefer taking core CS/AI coursework rather than ECE applied-AI courses?
• Does the admission committee automatically route applications to the appropriate department if the wrong one is selected?
• Does KAUST maintain a strict administrative boundary between CS and ECE for PhD applicants, or there is no such boundary?

Note that I tried to have a look at the courses to be studied in both (CS and ECE), but the website seems down.

Thanks in advance, any help is appreciated!

Hi all,

Has anyone here ever come across a remote PhD program in Computer Science where daily lab attendance isn't required—maybe just dropping in monthly or so, alongside mandatory weekly online check-ins?

If that's actually an option, what's the best way to reach out to potential advisors? It feels it is a stretch for a random guy from the internet to pitch this to a professor, so any tips on making that initial email convincing would be super helpful.

Also, how can you tell if an email sounds like it was written by AI? If I'm just using an AI tool to fix typos and polish the readability of my own draft, does that still count as AI-generated?

I’m working on my Statement of Purpose for PhD applications, and I feel stuck on how to frame my narrative.

It feels like the standard advice assumes you’re either:

• An applicant with existing publications who can say: "While working on Paper A, I found a gap in the literature that aligns with Professor X’s Paper P, and I want to solve it using method Y."
• The person with a cinematic origin story who had a 3 AM eureka moment in the lab or had their life changed forever by a single undergraduate lecture.

I don’t have published 1st author papers (but 2nd author in a paper and a patent in a different but related domain), and I don’t have a dramatic backstory. I’m just someone who genuinely enjoys the research process, wants the PhD to pursue research scientist roles in the future, and enjoys teaching/mentoring.

How do you compensate for a lack of formal publications in your SOP? More importantly, how do you pitch a solid, credible research direction to a potential PI without manufacturing fake passion or pretending to have had a life-altering revelation?

Would love to hear how others in a similar boat approached this.

I’m working on my Statement of Purpose for PhD applications, and I feel stuck on how to frame my narrative.

It feels like the standard advice assumes you’re either:

• An applicant with existing publications who can say: "While working on Paper A, I found a gap in the literature that aligns with Professor X’s Paper P, and I want to solve it using method Y."
• The person with a cinematic origin story who had a 3 AM eureka moment in the lab or had their life changed forever by a single undergraduate lecture.

I don’t have published 1st author papers (but 2nd author in a paper and a patent in a different but related domain), and I don’t have a dramatic backstory. I’m just someone who genuinely enjoys the research process, wants the PhD to pursue research scientist roles in the future, and enjoys teaching/mentoring.

How do you compensate for a lack of formal publications in your SOP? More importantly, how do you pitch a solid, credible research direction to a potential PI without manufacturing fake passion or pretending to have had a life-altering revelation?

Would love to hear how others in a similar boat approached this.

Hi everyone,

I’m planning to apply for CS PhD programs for the Spring 2027 cycle and would appreciate some advice on my strategy and profile.

I reached out to two prospective PIs with prominent labs (20k+ citations) but didn't receive a response. Should I still apply?

Also, what are the chances without top-tier conference publications (e.g., NeurIPS, ICML, ICLR, CVPR, ...). My background includes:

• 3 years of Research Engineer experience
• 2nd-author paper (AI + Telecom conference)
• 2nd-author patent

Thanks in advance for your insights!

Hi everyone,

I applied for the CS PhD last cycle and was rejected. I am looking to strengthen my research record before applying again. I’m currently an AI Research Engineer with 3 years of experience specializing in RL and LLM post-training (SFT, GRPO, LoRA).

Quick Profile

• Industry: 3 YOE (Python, C++, CUDA), production deployments, 1 co-authored patent, 1 corporate publication.
• Academia: MSc in Info Engineering (unranked/low-tier university) with an RL-focused thesis and 1 accepted conference paper.
• Projects & OSS: Light contributions to open-source LLM repos; regular paper replications with public GitHub repos and technical write-ups.

The Dilemma

I am planning to apply to KAUST primarily to gain top-tier research experience and strong LORs, then re-apply to PhD programs elsewhere.

• MS/PhD Track: Starts Jan 2027 (Fits my timeline much better).
• MS-Only Track: Starts Aug 2027 (Delays my plans by ~8 months).

Questions

1. Admissions: Is the acceptance rate significantly different between the MS-only and MS/PhD tracks at KAUST?
2. Mastering Out: If admitted to the MS/PhD track, is it feasible/acceptable to leave after completing the Master's to pursue a PhD elsewhere? Are there potential downsides (e.g., burnt bridges, refusal of strong LORs, or delays in receiving the Master's diploma)?

Any insights from current KAUST students, alumni, or applicants would be greatly appreciated!

I am an AI Research Engineer with 3 YoEs specializing in RL and LLM post-training (SFT, GRPO, and LoRA) from an African country targeting RE / MLE / AS roles at FAANG.

Professional & Academic Background:

• Developed and deployed medium-scale AI systems using Python, C++, and CUDA. Background in RAG pipelines, production chatbot deployments, and cloud infrastructure. Hold one co-authored patent and a corporate publication.
• Contributed (lightly) to open-source agent environments (CAMEL-AI, Unsloth) and regularly replicate research papers, documenting the results via technical blog posts and GitHub repositories.
• MSc in Information Engineering (from a low-tier uni) with a research thesis focused on RL and an accepted conference paper.

The Situation:

Despite tailoring my resume and utilizing internal referrals, I have not received responses from approximately 50 applications for Research Engineer (RE), Applied Scientist (AS), and Machine Learning Engineer (MLE) roles at big tech companies over the past six months.

My Questions:

1. Are top-tier PhD or Tier-1 publications like NeurIPS/ICML a must for FAANG-tier AI/MLE roles just to clear the initial resume screening stage?
2. Are there specific strategic adjustments I should make to my application approach, portfolio presentation, or resume formatting to improve response rates?

Coding used to be the cornerstone skill for tech roles — but with the rise of LLMs, that's shifting. Critical thinking and multitasking are becoming the new core competencies.

There's also a behavioral shift worth noting, people are moving away from piecing together answers via Google and going straight to LLMs for speed. I think search engines will see significantly less traffic as LLM search capabilities mature.

My bigger concern now is VLAs (Vision-Language-Action models). If robots reach the performance level of today's top models like Claude or Gemini, could that trigger physical laziness the same way LLMs may have fueled mental laziness?

A few questions I'd love to hear your thoughts on:

- What do you think will be the core skills for tech roles in the future?

- Is it ethical to keep pushing VLA development, or does it risk becoming an Oppenheimer moment — where the technology, once misused (think robotic armies), leads to regret?

Note: Ironically, I used an LLM to rephrase the post :) (language barriers)