I am looking for AI and Robotics jobs in Singapore. I want to make sure my resume is as competitive as possible for the Singapore tech market. I've focused heavily on including quantifiable metrics, live project links, and detailed tech stacks, but I know there is always room for improvement.

I really appreciate any tips, formatting advice, or market insights you can share!

Thanks in advance!

Hi everyone,
I'm hiring a remote AI/ML Engineer for ongoing work with U.S. clients. This role is open only to candidates currently based in the United States.

Requirements:

• 3+ years of hands-on experience in machine learning or AI engineering
• Strong experience with Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
• Experience building, training, and deploying ML models in production
• Experience working with data pipelines and large datasets
• Familiarity with cloud platforms (AWS, GCP, or Azure)
• Experience writing clean, testable code (unit/integration tests)
• Strong English communication skills
• Comfortable with client-facing work and video calls

Work Setup:

• Fully remote
• Ongoing, client-facing work
• Must be reliable and communicative

If you're interested, please send:

• Your location
• Years of experience
• ML/AI stack and tools you use
• Brief background in model development and deployment
• Availability

Budget: ($70–$80 HOURLY)

Hello! I recently graduated with a degree in math and have been applying for entry-level machine learning and data science roles. I completed an ML/Applied Math research internship, but I have been struggling to get interviews.

Here is my current (maybe incorrect) philosophy and some questions I specifically have:

I was hoping my math projects would stand out, since they are (mostly) grad level and probability focused, which I thought would be applicable for ML. But I'm starting to think they care less about this and more about my CS skills, particularly ML ops. Should I replace them?

I have gotten conflicting advice with my File manager job. I know it's not relevant, but I have been told that working there for 5 years (Mostly in high school) is worth expressing. I also attribute it to my organization skills which I like to express. Remove or keep?

I am a dual degree in finance, and it was a lot of work to get it. I hope it expresses some sort of dedication, so I have a finance program on there that I was chosen to participate in. Not relevant though. Remove?

I also am wondering how to demonstrate my ML stack. Right now, I have put my most technical knowledge. I.e. LSTM, Reservoir computing, CAEs, PINNs, and some time series models. I was particularly proud of my Dynamic Mode Decomp project since it seems to be lesser known, but alas I have no luck. Do I need to put more "foundational" stuff? It just seems a little silly to make a boring classifier model or K-NN model just to show I know it, but if I need to I will. Of course, I am willing to use whatever tool is needed on the job as simple is many times better, but if I did RC with ridge regression can I infer they know I know LASSO too? I personally would like to do my next project on more interesting things, like transformers and something rather than showing I know ML 101, but is that what is missing?

Also, I have a paper from my internship that will most likely be accepted for publication after we submit the recent reviewer comments. Should I make a small publications section, or just say published in xyz?

In any case, please grill me and provide any/all feedback possible. I need the wakeup call.

Hello AI/ML community, could you please critique my portfolio to suit employers. porfolio

From my previous post I came to know about CLOUD GPUs. But two question, who uses these Cloud GPUs? Like if a individual use, it gonna cost him a lot. For what purposes do they use? Like cloud gaming? Model running?

Hey, sharing something I've been testing – an AI capability assessment tool that scores you on ML/AI skills and gives structured feedback. It's in beta so it's free. If you're prepping for ML engineer roles or just curious where you stand, happy to share the link. 

有没有在准备回国求职/校招的朋友有这样的感受：面了不少，也拿到过几个面试机会，但被刷了之后真的很难判断到底哪里出了问题——是能力不够、人岗不匹配、还是只是发挥失常？

推荐一个正在内测的AI产品——才谱，专门帮有技术背景的同学生成一份「7D能力报告」，用AI面试+笔试的方式，把你的技术实现、逻辑思维、学习能力、自驱力等维度量化出来，可以直接作为求职材料补充。

比传统简历更能体现你真的会做项目，特别适合有AI/CS项目经验的人。

📌 内测阶段完全免费

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📌 目前在收集真实用户反馈，你的意见会直接影响产品迭代

感兴趣的同学可以直接评论或者私信我，我可以给你发送邀请码和内测链接～

Hey all,

We're looking for fully remote machine learning engineers. We accept all background / experience levels as long as you're competent.

This role is fully remote and you can work on your own schedule as long as targets are hit.

Apply here and we will get back to you within 24 hours!

http://parsewave.org/dev-application

Is there anyone Use Cloud GPU??

I am a AI/ML enthusiast, I been running models on my local cpu. But recently I heard about, Cloud GPUs. So what type of models we can run on these Cloud GPUs? Like I run models which can my local gpu handle, what about AI ML engineers??

Starting From Zero As A BA STUDENT Need guidance

“I’m a B.A. student and recently started getting into tech/coding. I want to build a career in the tech industry, especially in software/AI side, but honestly I’m confused about the proper roadmap.

Right now I’ve started learning Python fundamentals, but there’s so much information online that it gets overwhelming.

Can someone guide me step by step like:

what to learn first,

what skills actually matter,

how much maths is needed,

how to build projects,

and how to become job-ready from zero?

I don’t come from a tech background, so I’d really appreciate beginner-friendly advice from people already in the industry.”

Hi everyone,

I’ve been experimenting with an open-source project called BPFense AI focused on behavioral runtime detection and anomaly analysis.

Main areas I’m exploring:

• local AI inference
• telemetry-driven behavioral analysis
• TensorFlow + IsolationForest pipelines
• ensemble threat scoring
• deterministic replay validation

I’m mainly looking for technical feedback and research direction suggestions from people working in:

• AI security
• runtime detection
• malware behavior analysis
• adversarial ML
• eBPF/security telemetry

Project:
https://github.com/Anilk880/BPFense_ai_core

Would appreciate thoughts on:

• detection methodology
• dataset/replay strategies
• behavioral modeling approaches
• real-world runtime security use cases

Thanks.

I’m honestly getting really confused about my career path right now.

I spent a lot of time studying Machine Learning — math, ML algorithms, projects, some deep learning too — because I genuinely liked the field and thought it had a strong future. But everywhere I go now, I keep seeing people saying the ML/Data Science job market is really bad for freshers and that companies only want experienced people.

Now I’m questioning whether I made the right decision or not.

Some people are saying to start with Data Analytics first and then move into ML later. But even analytics feels uncertain now because AI tools are automating a lot of things.

So I wanted honest opinions from people already working in tech/data:

Is ML/Data Science really that bad for freshers right now?

Did I make a mistake focusing heavily on ML?

Should I switch my focus toward Data Analytics first?

What skills are actually helping freshers get hired in 2026?

Is the market just temporarily bad, or is the field becoming oversaturated?

On a scale of 1–10, how difficult is it for a fresher to get into ML/Data Science right now?

Please give honest opinions and real experiences, even if the truth is harsh. I just want a realistic understanding of the current market.

I am a Machine Learning Engineer with 3–4 years of experience, currently looking for a job opportunity with a salary of around ₹1.5L per month. Please refer me if there are any suitable openings.

Hello all, I’m looking for learn AI, ML on the entrepreneur side one -on- one from the best of the best from the scratch. Any recommendations will be highly appreciated.

Cincinnatus LLC is hiring experienced MLOps Engineers for a frontier AI project with a leading AI lab focused on next-generation large language models.

Type: W2 Contingent Role
Location: United States only (Remote)
Pay: $100 - $140 per/hr

Role:

• Improve large-scale ML training infrastructure and model performance
• Design and evaluate advanced MLOps and ML systems tasks
• Build evaluation frameworks for distributed training and kernel optimization

Criteria:

• Strong production experience with JAX or PyTorch at scale
• Experience with Pallas or Triton GPU kernel optimization
• Background in ML infrastructure, distributed systems, or MLOps engineering

Preferred experience: Experience at top-tier AI, ML, or infrastructure-focused organizations is highly valued.

Apply now - https://t.mercor.com/oEPRS

Ideal for ML systems engineers, distributed training experts, and AI infrastructure professionals.

A.Team is hiring a remote Senior Independent AI Engineer / Architect. Category: Software Development 💸Salary: $120 - $170 /hour 📍Location: Remote (Americas, Europe, Israel)

See more and apply here!

My brother is looking job into data science he has around 2.5-3 years of experience in Gen-AI.
If anyone can refer in their company, if 1st interview happens I will pay ₹10000 to that guy.

Only open for jobs in India.

I am a grad CS student at ASU, i have been applying a lot of jobs. I have been learning and working with AI/ML for past 6 years. I am also a recent graduate of bachelors at 2025. As a person who wants a break into this industry into a Intern/Co-Op on conversational LLM but however i am pretty good tackling with any data types/applications of it. How should i do it?

Roast my github so i can perfect it, i usually don't use it a lot before unless i deploy it.
https://github.com/Sehastrajit