LLM Research Intern

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

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?

• 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.

 
Compensation - $23/hour

Who can apply?

"This internship is supported by the Government of Canada's Student Work Placement Program (SWPP)”. To be eligible for SWPP funding, applicants must meet all three conditions given below:

• be a Canadian citizen, permanent resident, or person granted refugee protection in Canada;
• be enrolled as a full-time student at a recognized Canadian post-secondary institution for the duration of the placement [Dec 2026 to Apr 2027]; and
• be legally entitled to work in Canada. International students are not eligible for SWPP-funded placements.

Why ProCogia:

At ProCogia, you will work at the intersection of cloud, data, AI, and enterprise technology. You will solve real problems across commercial and regulated environments, work directly with clients, and take meaningful ownership from infrastructure design through production. You will also help shape the platform foundation supporting ProCogia’s growing AI capabilities, giving you exposure to technologies and challenges that extend well beyond traditional DevOps. This is an environment where strong engineers can build, learn, take ownership, and make a visible impact.