At a glance
Automatically prepared from the listing. Check the original description for the full requirements.
Jump to the original descriptionResponsibilities
Lead the technical foundation of Taskclan Intelligence by advancing the training, fine-tuning, and deployment of the T1 model family. Develop model-serving infrastructure, evaluation pipelines, and contribute to the multimodal and agentic AI roadmap.
Requirements
Requires strong expertise in Python, PyTorch, and transformer models with a proven track record of shipping AI systems or research. Candidates must be able to commit 15-20 hours per week initially in an equity-only capacity.
Working hours
20 hours per week
Work arrangement
Remote OK
Skills
- Python
- PyTorch
- Transformer-based models
- Fine-tuning
- Model Evaluation
- Hugging Face
- LoRA
- Quantization
- MLOps
- Model Serving
- Inference Optimization
- Dataset Construction
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Engineering
- Data & Analytics
- Science & Research
Keywords
- AI/ML Engineer
- Large Language Models
- T1 Core
- T1 Flow
- T1 Max
- Inference
- Quantization
- GPU
- Multimodal AI
- Agentic AI
- Model Safety
- Benchmarking
- Preference Datasets
- Cloud Infrastructure
- Open-weight models
Original job description
About Taskclan
Taskclan is a Canadian AI company building one connected ecosystem across Intelligence, Cloud, Studio, Engine, Enterprise and Lab.
Taskclan Intelligence develops our T1 model family: T1 Core, T1 Flow and T1 Max. These models will power capabilities across Taskclan Cloud, T1 Studio, our enterprise platform and products including GameNova, Nani, Forge3D and Spaces, TalentKit, ProposalAI and Ostrae.
The opportunity
We are looking for a Founding AI/ML Engineer to help build the technical foundation of Taskclan Intelligence. You will work directly with the founder on model development, evaluation, inference and deployment.
This is a hands-on founding role for someone who wants to build practical AI systems rather than simply integrate third-party APIs.
What you will own
• Advance the training and fine-tuning of T1 Core, T1 Flow and T1 Max
• Design reliable model evaluation, benchmarking and promotion pipelines
• Build high-quality training and preference datasets
• Improve inference performance, quantization and deployment efficiency
• Develop model-serving infrastructure for Taskclan Cloud and T1 Studio
• Strengthen model safety, security and production-readiness evaluations
• Monitor quality, latency, GPU usage and inference costs
• Contribute to our multimodal and agentic AI roadmap
• Document technical decisions and help shape the research culture
What we are looking for
• Strong experience with Python, PyTorch and transformer-based models
• Experience fine-tuning or evaluating open-weight language models
• Familiarity with Hugging Face, LoRA, quantization and GPU environments
• Understanding of inference systems, model serving and MLOps
• Evidence of shipped AI systems, research, open-source work or strong personal projects
• The ability to work independently and make evidence-based technical decisions
• The ability to commit approximately 15–20 hours per week initially
• At least three hours of collaboration availability with Eastern Time on agreed working days
Compensation and commitment
This is initially a part-time, equity-only founding role. There is no cash salary at present. The proposed equity range is 1.5%–2.5%, depending on experience, commitment and the final scope of the role.
Any equity grant will be governed by a formal agreement, four-year vesting, a one-year cliff and board approval. Our intention is to transition the position into a paid role once Taskclan secures sufficient financing or recurring revenue.
Why join now
You will help define Taskclan’s model architecture, evaluation standards, deployment infrastructure and long-term technical direction. Your work will influence every pillar and product across the Taskclan ecosystem.
To apply, share a model, research project, repository or production AI system you have built. Explain the problem, your specific contribution, the technical decisions you made and the results you achieved.
