AI Researcher

Location: Remote

Duration: 2–4 months (project-based)

Type: Contract / Research Collaboration (Paid)

About the Project

We are looking for a Master’s or PhD student to work on fine-tuning large language models (LLMs) for domain-specific tasks. The goal is to take an existing pretrained model (e.g., Meta AI’s LLaMA-class models or similar) and specialize it for a narrow, high-value use case using efficient fine-tuning techniques.

This is a hands-on applied project designed for someone who wants real-world experience deploying and optimising LLM systems.

Help drive the next wave of applied AI by demonstrating how fine-tuned LLMs can unlock advanced, real-world use cases beyond general-purpose foundation models. Organizations that require domain-specific accuracy, self-hosted deployments, customisable workflows, or performance beyond out-of-the-box capabilities increasingly rely on fine-tuned models to meet those needs.

Through this project, you will contribute to building specialised AI systems that deliver improved accuracy, efficiency, and control compared to out-of-the-box models. You will also help bridge the gap between academic knowledge and real-world application by applying fine-tuning techniques to solve concrete business problems.

What You’ll Work On

Fine-tuning pre-trained LLMs on small to medium datasets (500–20k examples)

Implementing parameter-efficient fine-tuning (e.g., LoRA-style methods)

Optimising training for cost and performance

Running experiments on GPU cloud infrastructure

Evaluating model performance and tradeoffs (specialisation vs generalisation)

Deploying fine-tuned models for inference

Experience

Strong Python skills

Experience with deep learning frameworks: PyTorch (preferred) or TensorFlow

Experience with Hugging Face Transformers or similar ecosystems

Hands-on experience training or fine-tuning transformer models on GPUs (local or cloud-based)

Previous experience using cloud platforms for model training or deployment (e.g., AWS, GCP, Azure, RunPod or similar GPU providers)

Experience working with or fine-tuning open-weight LLM families (Gemma-3, Qwen-3.5, Llama 4, GPT-OSS, Mistral...)

Hands-on experience with LoRA

Understanding of:

Fine-tuning vs pretraining

Overfitting and generalization

Model evaluation

Strong business awareness: ability to understand the context of the fine-tuning task and translate domain requirements into clear modeling objectives

What you bring

MSc or PhD student in Computer Science, Machine Learning, AI, or related field

Alternatively, 6 months of hands-on experience training and fine-tuning deep learning models

Has worked on LLMs in research or industry

Has fine-tuned at least one transformer model

Comfortable working independently

Interested in applied AI and real-world constraints (cost, latency, memory)

What You’ll Gain

Real-world experience fine-tuning large models (30B–100B parameter class)

Exposure to production constraints and deployment

Opportunity to co-author technical writeups if applicable

Strong applied portfolio project

What We Offer

100% Remote Work: Work from anywhere with flexibility and autonomy

Dynamic, High-Impact Projects: Work on cutting-edge ML and GenAI solutions across diverse industries

International Clients: Collaborate with global organizations and solve real-world challenges at scale

Urban Sports Club Membership: Supporting your physical and mental wellbeing

Monthly Bolt Credits: For rides

Company Events & Offsites: Regular team gatherings to connect, collaborate, and celebrate