Machine Learning Intern – LLM FineTuning

About the role
We're looking for a curious, hands-on engineer to join our team as an LLM Fine-Tuning Intern. You'll help adapt open-source large language models to real product tasks: preparing training data, fine-tuning models with LoRA and QLoRA, evaluating results carefully, and helping ship fine-tuned models to production.
This is a 3-month paid internship with a stipend of ₹15,000 per month. Based on your performance during the internship, you'll be considered for conversion to a full-time role.
It's a learning-heavy role. You'll work alongside experienced engineers and own real experiments from day one.
Internship details
- Duration: 3 months
- Stipend: ₹15,000 per month
- Conversion: Full-time offer based on performance
- Start date: Immediate
What you'll do
- Build and clean instruction and chat datasets: formatting, deduplication, quality checks, and train/eval splits.
- Fine-tune small and medium open models (1B–8B) with supervised fine-tuning (SFT) using LoRA and QLoRA, mainly with Unsloth, Hugging Face TRL, PEFT and Transformers.
- Run controlled experiments on rank, learning rate, data mix and sequence length, and record the results clearly.
- Evaluate models with held-out test sets, task metrics (accuracy, exact match, format validity) and LLM-as-judge, and check that general abilities haven't regressed.
- Debug training runs: loss curves, chat templates, label masking, GPU memory problems.
- Export and package models (merged 16-bit, GGUF) for serving with tools like vLLM, llama.cpp or Ollama.
- Keep up with post-training methods such as DPO, ORPO and GRPO, and help try them on our use cases.
Must have
- A degree in CS, AI/ML, Data Science or a related field (final-year students are welcome to apply).
- Strong Python. Comfortable with PyTorch or similar.
- A clear understanding of the basics: how transformers work at a high level, tokens, loss, overfitting, and train/validation/test splits.
- Can explain LoRA and QLoRA, and why they reduce memory.
- Has fine-tuned at least one model on their own: a course project, a Kaggle or Colab notebook, a hackathon or a personal project.
- Careful with data, and honest about results, including negative ones.
Nice to have
- Hands-on experience with Unsloth, TRL/PEFT or the Hugging Face Hub.
- Awareness of preference tuning (DPO) or RL-based methods (RLHF, GRPO).
- Experience with experiment tracking (Weights & Biases, MLflow) and basic Linux/GPU environments.
- A GitHub repo, blog or model card showing something you've built.
What you'll learn
- The full post-training lifecycle: data → SFT → preference tuning → evaluation → deployment.
- How to run disciplined experiments and make decisions from evidence.
- Serving and optimizing models in production.
Hiring process
1. Application review (a GitHub link or project write-up is strongly encouraged)
2. 30-minute screening call
3. Take-home or live task: fine-tune a small model on a provided dataset and compare it with the base model
4. 45-minute technical interview (fundamentals, LoRA/QLoRA, data, debugging)
How to apply
Send your resume to support@vima3ya.com, along with a GitHub link or project write-up showing a model you've fine-tuned.

Work arrangement
Yes