LLM Research Intern

About ProCogia 

ProCogia is a data and AI consulting firm helping organizations turn complex technology challenges into measurable business outcomes. We work with clients across highly regulated and high-stakes industries, including telecommunications, financial services, life sciences, healthcare, and the public sector, where security, governance, reliability, and performance matter.

Our teams combine data science, engineering, AI, analytics, and consulting expertise to build practical, production-ready solutions. We are actively advancing capabilities across generative AI, large language models, model adaptation, agentic systems, AI governance, and enterprise AI applications.

At ProCogia, interns work alongside experienced practitioners on meaningful technical problems and contribute directly to research and experimentation that informs real-world AI solutions.

Our Core Values: Trust, Growth, Innovation, Excellence, and Ownership.

Location: Vancouver (On-site)  

Job Summary 

ProCogia is looking for a curious and technically strong LLM Research Intern to join our Vancouver team. This is a hands-on research and engineering role for someone interested in understanding how large language models can be adapted, evaluated, optimized, and deployed for specialized enterprise use cases. You will work alongside our AI, data science, and engineering teams to experiment with open-weight LLMs, supervised fine-tuning, continued pretraining, parameter-efficient adaptation, model evaluation, RAG, and inference optimization.

Key Responsibilities 

• Assess domain-specific datasets and determine the right adaptation approach across fine-tuning, continued pretraining, RAG, or hybrid strategies.
• Establish baseline performance and measure whether model adaptations produce meaningful, defensible improvements.
• Curate, clean, deduplicate, structure, and quality-filter datasets for training and evaluation.
• Fine-tune open-weight LLMs using techniques such as LoRA, QLoRA, PEFT, and multi-adapter approaches.
• Support training and experimentation across single-GPU and distributed multi-GPU environments.
• Design rigorous evaluation frameworks covering factuality, reasoning, grounding, robustness, domain performance, and failure modes.
• Apply public benchmarks and build task-specific evaluation datasets and metrics where standard benchmarks are insufficient.
• Compare models, prompting strategies, retrieval methods, and adaptation techniques through controlled experiments.
• Analyze trade-offs across model quality, accuracy, latency, memory, GPU utilization, token usage, compute requirements, and cost.
• Improve model reliability by evaluating RAG, grounding, reranking, guardrails, hallucination-reduction techniques, and inference optimization.
• Research emerging models, papers, datasets, benchmarks, and techniques, and communicate findings through reproducible experiments, documentation, demos, and presentations.
  

What You Bring

• Currently enrolled in or recently completed a Bachelor’s, Master’s, or PhD program in Computer Science, Machine Learning, AI, Data Science, Computational Linguistics, or a related field.
• Strong programming skills in Python.
• Hands-on experience with machine learning or deep learning through research, coursework, internships, projects, or open-source contributions.
• Solid understanding of transformer architectures, LLMs, and modern NLP concepts.
• Experience or exposure to model training and fine-tuning using tools such as PyTorch and Hugging Face Transformers.
• Understanding of experimental design, benchmarking, model evaluation, and data quality.
• Working knowledge of model size, GPU memory, context length, training compute, inference performance, and cost trade-offs.
• Strong analytical and problem-solving skills with the ability to challenge assumptions and interpret experimental results.
• Ability to read technical research and translate new ideas into practical, testable experiments.
• Strong communication, documentation, curiosity, ownership, professionalism, and ability to learn quickly.

 
Nice to Have

Experience with any of the following is an asset, but not required:

• • LoRA, QLoRA, PEFT, or other parameter-efficient fine-tuning approaches
  • Multi-GPU or distributed training using DeepSpeed, FSDP, Accelerate, or similar frameworks
  • LLM evaluation frameworks and public benchmarks
  • Inference frameworks such as vLLM, TGI, or TensorRT-LLM
  • Quantization, pruning, or model compression
  • CUDA, GPU optimization, or high-performance computing
  • Research publications, technical blogs, open-source contributions, or documented LLM projects

Compensation - $23/hour