LLM Research Intern

Position: LLM Research Intern (December 2026 to April 2027)
If you’re looking for a dynamic and diverse place to work, where the environment supports continual learning and offers endless opportunities, then a Pro Cogia career could be the one for you.

LLM Research Intern (December 2026 to April 2027)
About Pro Cogia:

Pro Cogia is a data and AI consulting firm helping organizations turn complex technology challenges into measurable business outcomes. We work across highly regulated and high-stakes industries, including telecommunications, financial services, life sciences, healthcare, and the public sector, where security, governance, reliability, and performance are critical. Our teams bring together expertise across data science, engineering, AI, analytics, and consulting to design and deliver practical, production-ready solutions.

We are actively advancing capabilities across generative AI, agentic systems, workflow automation, AI governance, and enterprise AI applications. At Pro Cogia, you will work alongside experienced practitioners on meaningful technical challenges and contribute directly to both client solutions and the platforms and products we are building internally.

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

Work Location:

Vancouver (On-site – Full-Time Work from office in Vancouver, five days a week Mon-Fri)

Job Summary

Pro Cogia 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?

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,…