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Engineering · AI

Software Intern (LLM Fine-Tuning)

LocationRemote / Hybrid · Greater Toronto Area
TypeInternship · Fall/Winter
Reports toChief Product Officer
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About the Role

We are looking for a Software Intern to help us adapt open-weight large language models to security and identity work. You will take a model from raw data to a measured result: preparing datasets, running fine-tuning jobs, and building the evaluations that tell us whether a change helped.

This is a Fall/Winter internship: hands-on work with real models and real GPUs, alongside the founding team. If you have already fine-tuned a model and want to do it on problems that matter, this is the role for you.

Responsibilities

What you’ll own.

Data & training

  • Collect, clean, and prepare training and evaluation datasets.
  • Fine-tune open-weight LLMs with SFT and parameter-efficient methods (LoRA / QLoRA).
  • Run and monitor training jobs on GPUs, and make the most of limited compute.

Evaluation

  • Build evaluation sets and benchmarks that reflect the tasks we care about.
  • Compare models and training runs, and report clearly what worked and what did not.
Required Skills

What you’ll bring day one.

Core

  • PyTorch and the Hugging Face ecosystem (Transformers, Datasets, PEFT).
  • Hands-on experience with open-weight LLMs.
  • Fine-tuning: SFT, LoRA / QLoRA.
  • Dataset preparation for training and evaluation.
  • Model evaluation.
  • GPU fundamentals: memory, batch sizes, mixed precision.
Nice to Have
  • Domain-adaptive or continued pretraining (DAPT).
  • Synthetic data generation.
  • CUDA.
  • Docker.
  • vLLM for serving and fast inference.
  • Experiment tracking (Weights & Biases, MLflow or similar).
  • Evaluation frameworks and harnesses.
  • Reinforcement learning for LLMs.
Why Join Us
01

Train and evaluate real models, not a sandbox project.

02

Work directly with the founding team.

03

Learn how AI meets security and cryptographic identity.

Ready to apply?

Email your application directly to FR03@arkion.ai. Include the following so we can move quickly:

  • Resume
  • GitHub or portfolio
  • One model you fine-tuned or evaluated: what you changed, how you measured it, and what you learned
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