 LLM Research Intern

VancouverOn-site3w ago

Seniority: Junior

About the role

<div class="content-intro">About ProCogia:  <div class="elementToProof">We help businesses transform data into real growth!</div> <div class="elementToProof"> </div> <div class="elementToProof">Our clients operate in high-stakes, highly regulated industries (such as telecom, financial services, life sciences, and more), where precision, compliance, and measurable outcomes are non-negotiable. We partner with them by embedding expert data science, engineering, and AI talent directly into projects that matter.</div> <div class="elementToProof">We’re a diverse, close-knit team with a shared goal: delivering top-class, end-to-end data solutions. We don’t just analyse data, we push the boundaries of what’s possible, helping clients unlock new value and insights.</div> <div class="elementToProof"> </div> <div class="elementToProof">When you join ProCogia, you’ll find a supportive, growth-driven environment where your ideas are welcomed, and your development is prioritized. We offer competitive salaries, generous benefits and perks for personal and professional development. </div> <div class="elementToProof"> </div> <div class="elementToProof">If you’re ready to unleash your potential and work at the cutting edge of data consulting, we’d love to meet you!</div> The core of our culture is maintaining a high level of cultural equality throughout the company. Our diversity and differences allow us to create innovative and effective data solutions for our clients.  Our Core Values: Trust, Growth, Innovation, Excellence, and Ownership</div>Job Title: LLM Research Intern  Job Summary  Reporting directly to the AI Solutions Specialist, ProCogia is looking for a curious and driven LLM Research Intern to join our team in Vancouver, BC. In this role, you will work alongside our data science and AI engineering teams to research, experiment with, and evaluate large language models for real-world client applications. You'll have the opportunity to dig into the latest developments in generative AI, from prompt engineering and fine-tuning to retrieval-augmented generation (RAG) and model evaluation, while contributing to meaningful projects that push the boundaries of what's possible with LLMs.  This is a hands-on internship designed for someone who is passionate about NLP and AI research, eager to bridge the gap between cutting-edge academic work and practical implementation, and excited to grow in a collaborative, fast-paced consulting environment.  This is an in-person role, 40 hours/week, based in our beautiful downtown Vancouver office. The internship is from June-August, with the possibility to extend until December based on performance and business needs.   Responsibilities  <ul> <li data-leveltext="" data-font="Symbol" data-listid="1" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="1" data-aria-level="1">Assess client-specific data assets and determine the appropriate adaptation strategy — continued pretraining, supervised fine-tuning, or a combination — based on the domain, data volume, and use case requirements </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="1" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="2" data-aria-level="1">Curate, clean, structure, and prepare domain-specific datasets from raw client data for use in model training pipelines </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="1" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="3" data-aria-level="1">Fine-tune large language models in the 70B–100B+ parameter range using techniques such as LoRA, QLoRA, and multi-adapter patterns </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="1" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="4" data-aria-level="1">Perform continued pretraining on open-weight models (Qwen, Llama, and related ecosystems) to embed domain knowledge directly into model weights </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="1" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="5" data-aria-level="1">Manage distributed training workflows across multi-node GPU clusters </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="1" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="6" data-aria-level="1">Design and execute evaluation frameworks to validate domain adaptation quality, factual grounding, and model behavior </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="1" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="7" data-aria-level="1">Support RAG system development where applicable, including vector database integration, chunking strategies, and reranking pipelines </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="1" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="8" data-aria-level="1">Contribute to inference optimization and deployment pipeline integration </li> </ul> Required Qualifications  <ul> <li data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="1" data-aria-level="1">Currently enrolled in or recently completed a Bachelor's, Master's, or PhD program in Computer Science, Machine Learning, or a related field </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="2" data-aria-level="1">Demonstrated hands-on experience fine-tuning large language models, supported by concrete project work, research, or open-source contributions </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="3" data-aria-level="1">Experience with frontier-scale models (100B+ parameters) or distributed training across multi-node GPU clusters </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="4" data-aria-level="1">Familiarity with parameter-efficient fine-tuning methods (LoRA, QLoRA) and open-weight model architectures </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="5" data-aria-level="1">Experience with data curation and preparation workflows for LLM training, including cleaning, formatting, deduplication, and quality filtering </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="6" data-aria-level="1">Proficiency in Python-based ML frameworks such as PyTorch, HuggingFace Transformers, DeepSpeed, or FSDP </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="7" data-aria-level="1">Understanding of training compute, memory constraints, and inference trade-offs at scale </li> </ul> Nice to Have  <ul> <li data-leveltext="" data-font="Symbol" data-listid="3" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="1" data-aria-level="1">Familiarity with RAG architectures or production inference serving frameworks (vLLM, TGI, TensorRT-LLM) </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="3" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="2" data-aria-level="1">Experience in low-resource or multilingual NLP settings </li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="3" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"multilevel"}" data-aria-posinset="3" data-aria-level="1">Relevant publications, open-source contributions, or documented projects involving LLM training. </li> </ul> Compensation  <ul> <li data-leveltext="" data-font="Symbol" data-listid="4" data-list-defn-props="{"335552541":1,"335559683":0,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="1" data-aria-level="1">$23/hour </li> </ul> ProCogia is proud to be an equal-opportunity employer. We are committed to creating a diverse and inclusive workspace. All qualified applicants will receive consideration for  employment without regard to race, national origin, gender, gender identity, sexual  orientation, protected veteran status, disability, age, or other legally protected status. <div class="content-conclusion">ProCogia is proud to be an equal-opportunity employer. We are committed to creating a diverse and inclusive workspace. All qualified applicants will receive consideration for employment without regard to race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. </div>

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