LLM Research Intern (December 2026 to April 2027)

ProCogia, Vancouver, BC (on-site) +1

What you will do

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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.