LLM Research Intern (December 2026 to April 2027)
ProCogia, Vancouver, BC (on-site) +1
What you will do
- 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.