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Arkion Identity Systems

Software Intern

Toronto, ON, Canada
Greater Toronto Area, Canada, Canada

Internship

Apply About employer

Applications open in a new tab.

Job details

Job type: Internship
Apply by: Nov 26, 2026
Posting language: English
Posted: Oct 28, 2026

Original job description

ARKION

Platform

ArkionID
AgentID
MachineIDsoon

Resources

Learn

Use Cases
NHIG: The Complete Guide
The NHIG Standard
The Vocabulary
Field Notes
Why Now

Webinars and white papers

Webinars
White papers

Company

About Arkion
Team
CareersWe’re hiring
5 roles
Trust Centersoon
Contact

ScanDemo

PlatformResourcesCompany

ScanDemo

← All open roles

Engineering

Software Intern (LLM Fine-Tuning)

LocationRemote / Hybrid

Greater Toronto Area

TypeInternship

Fall/Winter

Reports toChief Product Officer

Apply for this role→

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

Apply by email→← All open roles

ARKION

Identity Infrastructure for an Autonomous World.

ArkionID manages certificates from issue to revocation. AgentID builds on it to give every AI agent its own short-lived ID. MachineID is next.

Product

ArkionID
AgentID
MachineIDSoon
Use Cases

For Executives

Executive Brief
Compliance
Cost of Inaction

Learn

Non-human identity guide
Our governance standard
Vocabulary
Field Notes
AI Agent Governance
Certificate Lifecycle
Identity Registry

Company

About
Team
Careers
Book a Demo

Trust & Legal

Trust Center
Security
Privacy
Terms

Apply About employer

More about this job

Responsibilities

Prepare training and evaluation datasets, fine-tune open-weight large language models using supervised and parameter-efficient methods, and run GPU training jobs. Build benchmarks, compare models and training runs, and clearly report evaluation results.

Requirements

Candidates should have hands-on experience with open-weight LLMs, PyTorch, and the Hugging Face ecosystem, including Transformers, Datasets, and PEFT. They should also have experience with fine-tuning, dataset preparation, model evaluation, and GPU fundamentals such as memory, batch sizes, and mixed precision.

Skills

PyTorch
Hugging Face Transformers
Hugging Face Datasets
PEFT
Open-Weight LLMs
Supervised Fine-Tuning
LoRA
QLoRA
Dataset Preparation
Model Evaluation
GPU Fundamentals
Domain-Adaptive Pretraining
Synthetic Data Generation
CUDA
Docker
vLLM

Visa sponsorship

Not detected in the job text

Keywords

Large Language Models
Open-Weight Models
PyTorch
Hugging Face Transformers
Hugging Face Datasets
PEFT
Supervised Fine-Tuning
LoRA
QLoRA
Dataset Preparation
Training Datasets
Evaluation Datasets
Model Evaluation
Benchmarks
GPU Training
GPU Memory
Batch Sizes
Mixed Precision
Domain-Adaptive Pretraining
Synthetic Data Generation
CUDA
Docker
vLLM
Experiment Tracking
Weights & Biases
MLflow
Reinforcement Learning
Security
Cryptographic Identity

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