Posted 17 days ago
Software Intern
@ Arkion-tech
Toronto, Ontario
SIGNALS
Requirements summary:
- Required Skills
- Hands-on experience with open-weight LLMs.
Source and dates
Source type not verified · apify-linkedin
- Employer posting date
- 2026-10-27
- First seen by our source
- 2026-11-14
- Source record updated
- 2026-11-14
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Arkion-tech
Job description
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
What you’ll bring day one.
Core
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.
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.
- 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.
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.
- 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.
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.
Journal
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