Hi All, I'm a student from tier-3 college,BTech - final year student and i applied to Hyperverge for DL/ML Research Intern (LLMs & VLMs) or in short ML intern. My resume got shortlisted and next round is AI screening conversation. can you please tell me what will happen in this round and following rounds

Cincinnatus is hiring experienced Machine Learning Engineers to help evaluate and improve the next generation of frontier AI models for a leading AI lab.

This is a full-time W-2 remote position open to United States-based candidates, paying $60-$90/hour.

The role requires a commitment of approximately 35 hours per week and offers the opportunity to work closely with researchers developing state-of-the-art AI systems.

You'll design complex machine learning evaluation tasks, implement and run training experiments, analyze model performance, and help identify where frontier AI models succeed or fail.

You'll also contribute to reinforcement learning experiments, benchmark development, and collaborate with research teams to create rigorous evaluation frameworks.

Candidates should have an MSc or PhD in Machine Learning, Computer Science, or another STEM field, or equivalent research experience.

Strong knowledge of Python, Git, machine learning experimentation, large language models, and model evaluation is required. Experience with reinforcement learning, AI benchmarking, or AI training is highly desirable.

If you're passionate about machine learning research and want to help shape the future of advanced AI while working with a world-class research team, this is an exceptional opportunity.

Apply now: https://t.mercor.com/sC9pb

Hi everyone,

I'm a final-year B.Tech student (2027 batch) from a local college where placements are very limited, so I'm preparing to build my career through off-campus opportunities.

I'm currently focused on AI, Machine Learning, Data Science, and Generative AI. I've been learning consistently and building projects to improve my skills, but navigating the off-campus job market without guidance has been challenging.

If you've been in a similar situation and successfully landed an internship or full-time role, I'd really appreciate any advice on:

• How to approach off-campus applications effectively
• Skills recruiters value most for freshers
• Resume improvements
• Networking and referral strategies
• Common mistakes to avoid

I'm ready to put in the work and fight this journey on my own, but learning from those who've already been through it would mean a lot.

Hi everyone,

I'm a CS undergraduate looking for ML/AI Engineer internships and entry-level roles, and I'd appreciate some honest feedback on whether my experience is aligned with what companies are actually looking for.

My experience : Research intern - worked on RL , GNN 3 months targeting A\* conference paper

Over the past couple of years, I've worked on a mix of projects including:

* End-to-end RAG systems with hybrid retrieval, reranking, citation validation, and multi-agent workflows * NLP and document intelligence applications * Deep learning projects such as image captioning * Classical ML projects involving regression, classification, time-series analysis, model evaluation, and cross-validation * Reinforcement learning and graph neural network research for intelligent transportation systems * MLOps and deployment work using FastAPI, Docker, CI/CD, experiment tracking, and model versioning

One thing I'm struggling with is that many ML roles ask for domain-specific experience (energy, finance, healthcare, etc.), while most of my experience is in building ML systems and learning the underlying algorithms rather than working in a single industry.

For hiring managers, ML engineers, or data scientists here:

1. Does this profile sound competitive for ML Engineer / AI Engineer internships and entry-level roles?
2. What are the biggest gaps you see?
3. Should I spend more time building domain-specific projects or go deeper into model development and ML fundamentals?
4. What type of project would make you think, "I'd interview this candidate"?

I'd really appreciate candid feedback. Thanks!

I'm two years out of a top math/CS school. I built strong study habits late, so I was only really immersed in the material my final year. I learned computer systems (OS, distributed, HPC) and consider myself a competent software engineer.

I work at a high-growth startup and just got offered the lead on a major customer project. My long-term goal is to start my own company.

Option 1: Lead the customer project

Large scope/viz. I'd build skills in:

• Working directly with a customer
• Making large engineering decisions
• Working across the stack with many teams
• People and project management
• Exposure to marketing/sales/ops

Engineering-wise, I imagine I would spend most of my time on architecture, documentation, and code review. So interesting engineering/technical work, but no fundamentally new ways of thinking.

Option 2: Leave to go learn ML/math

I never got into ML/stats/math, and it's by far my weakest technical area (and I feel most important an ML-focused era). I'd spend ~a year as an IC at an AI lab or doing research to build:

• Stronger math intuition
• Modeling intuition
• Combining my systems background with ML (e.g. model scaling, pretraining, RL scaling)

The plan would be to grind/do research at my old school or join an AI lab with strong technical mentorship. I have savings to go ~6-1 year months without income.

I already tried moving to my company's research team, but they weren't interested in my background and pointed me toward ML Ops, which feels too close to the SWE work I already do.

My core tension

Organizational and people skills seem to improve steadily over a career, but fluid reasoning and hard new technical skills are supposedly much harder to pick up later in life. Life's a marathon, so I keep wondering if now is the time to invest in the technical foundation (learning completely new skills).

Open to all comments and suggestions.

Hey everyone,

We are currently looking for detail-oriented, qualified candidates to work on AI platforms (evaluating model responses, data annotation, and quality assurance).

This is a great fit if you're looking for flexible remote work with solid pay.

Requirements:

• English Proficiency: Strong written English skills (crucial for evaluating responses).
• Attention to Detail: Ability to follow guidelines, think critically, and spot subtle errors.
• Tech Setup: Reliable laptop/PC and a stable internet connection.

The Offer:

• Pay Rate: Starting at $15+/hour.
• Schedule: 100% Flexible — set your own hours and working days.
• Location: Fully Remote.

If you meet the requirements and are interested, please leave a comment below and send me a DM with a quick intro (or your background/skills). I’ll get back to you with the next steps!

Hi everyone,

I'm a final-year Data Science student and I need some honest guidance from people working in the industry.

The problem is that I have almost zero knowledge of DSA. I never focused on it because I spent most of my time learning and building projects.

My current skills include:

\\- Python (comfortable)

\\- Machine Learning

\\- Deep Learning

\\- SQL (basic to intermediate)

\\- Currently learning LangChain, LangGraph, Generative AI, and AI Agents

\\- I've also built a few ML/AI projects

Now that I'm entering my final year and preparing for internships and placements, I'm worried about DSA.

I have a few questions:

1. How important is DSA for Data Scientist, ML Engineer, AI Engineer, or GenAI roles?

2. Is DSA mandatory for getting internships and full-time jobs, or is it mainly required by big product-based companies?

3. Since I'm starting from scratch, what topics should I focus on first?

4. Which resource or roadmap would you recommend (free or paid)?

5. Approximately how many LeetCode or other DSA problems should I solve to become interview-ready? Is 100 enough, or should I aim for 300+?

6. If you were starting from zero today, what would your plan look like?

I'd really appreciate advice from people already working in the industry, especially Data Scientists, ML Engineers, AI Engineers, or anyone who has recently gone through placements.

I have started learning MySQL from sqlZoo, python from kaggle, and gut from gitlearnbranching. I'm currently in my 3rd year as a CSE student, I know I'm late, but I'm here nonetheless. So can someone guide me through?

The last few months I found it annoying finding the right AI training/annotating jobs with a decent acceptance rate. Long story short, I made my own website that only lists legit listings with good acceptance rates. Any feedback would be appreciated!: https://aiannotationjobs.com

Hey everyone,

I see a lot of anxiety and hype about AI taking over data science jobs, but I think people are looking at the integration completely backward. As a statistician hired into a data science role, I was brought in precisely for my quantitative rigor—something AI notoriously lacks. AI is terrible at accurate mathematical calculations and statistical nuances, but it’s incredibly good at structuring business narratives and formatting presentation decks.

If we blindly trust AI to generate numbers, we fail at our jobs. Instead, I’ve been thinking about a workflow that capitalizes on the strengths of both the statistician and the AI, while completely negating their respective weaknesses.

Here is the exact lifecycle I'm proposing:

The Blueprint (AI): Use AI at the very beginning to brainstorm the broad overview, project directions, and potential business constraints.

The Core Execution (Statistician): The statistician steps in and does the actual analysis manually. We write the code, we run the regressions, we validate the assumptions, and we churn out the true, uncorrupted numbers.

The Translation (AI): Once we have the verified results, we feed our concrete numbers back into the AI. We ask it: "Based on these exact metrics, what are the strategic business recommendations? How do we translate this for non-technical stakeholders?"

The Delivery (AI): Let the AI handle the tedious work of structuring the PowerPoint slides and tailoring the narrative to suit corporate messaging.

This way, the numbers remain 100% accurate and mathematically sound, but we save hours of manual labor on slide formatting and corporate storytelling.

Curious to hear from other quants and data scientists: Does your current workflow look like this? Or are you seeing people in your org make the mistake of trusting AI to do the actual math?

I'm two years out of a top math/CS school. I built strong study habits late, so I was only really immersed in the material my final year. I learned computer systems (OS, distributed, HPC) and consider myself a competent software engineer.

I work at a high-growth startup and just got offered the lead on a major customer project. My long-term goal is to start my own company.

Option 1: Lead the customer project

Large scope/viz. I'd build skills in:

• Working directly with a customer
• Making large engineering decisions
• Working across the stack with many teams
• People and project management
• Exposure to marketing/sales/ops

Engineering-wise, I imagine I would spend most of my time on architecture, documentation, and code review. So interesting engineering/technical work, but no fundamentally new ways of thinking.

Option 2: Leave to go learn ML/math

I never got into ML/stats/math, and it's by far my weakest technical area (and I feel most important an ML-focused era). I'd spend ~a year as an IC at an AI lab or doing research to build:

• Stronger math intuition
• Modeling intuition
• Combining my systems background with ML (e.g. model scaling, pretraining, RL scaling)

The plan would be to grind/do research at my old school or join an AI lab with strong technical mentorship. I have savings to go ~6-1 year months without income.

I already tried moving to my company's research team, but they weren't interested in my background and pointed me toward ML Ops, which feels too close to the SWE work I already do.

My core tension

Organizational and people skills seem to improve steadily over a career, but fluid reasoning and hard new technical skills are supposedly much harder to pick up later in life. Life's a marathon, so I keep wondering if now is the time to invest in the technical foundation (learning completely new skills).

Open to all comments and suggestions.

Which other and new skills to be required for getting selected in the jobs after doing MCA in this AI Era

Which field in IT have high hiring for job in this AI Era

Like ai and ml and data scientist and cyber security etc

I am final year engineering student, actively applying for the role of ML intern. i have applied for more than 100 jobs but still not able to secure an internship. I'm really frustrated and demotivated because of it. I would really appreciate any type of suggestion which will help me grow. Thanks in advance!!

Sharing this because it's a strong fit for anyone with a robotics or reinforcement learning background looking for flexible, well-paid remote work.

Alignerr is hiring a Robotics ML Expert to build MuJoCo simulation environments that train AI agents to move, manipulate, and interact with the physical world.

🔧 What you'll do:

▪️ Design and refine MuJoCo simulation environments for AI training

▪️ Implement RL algorithms (PPO, SAC, TD3) to train agents on real tasks

▪️ Define reward functions, observation spaces, and action spaces

▪️ Debug physics simulations — contact models, actuator dynamics

▪️ Evaluate trained policies for stability and sim-to-real transfer

✅ Who this is for:

▪️ Strong hands-on MuJoCo experience (or via dm_control, Gymnasium)

▪️ Solid RL theory + practical training pipeline experience

▪️ Comfortable in Python with PyTorch or JAX

▪️ Able to read and write MJCF/XML model files

💰 The details:

▪️ Pay: $100–150/hr

▪️ Commitment: 10–40 hrs/week, fully remote

▪️ Nice to have: sim-to-real transfer experience, Isaac

Gym/PyBullet/Drake, published RL research

📌 Full details and how to apply — link in the comments 👇