Optimize machine learning systems across software and hardware while applying Python, deep learning, and full-stack systems thinking.

As a Modelling Resident, you will pursue system-wide efficiency gains across software, hardware, and algorithmic domains. Your work connects model optimization with real-world product impact. Relevant projects may involve synthetic data, interfaces, real-time alignment, or algorithmic optimization.

Key Responsibilities:

- Drive efficiency gains across complete technical systems
- Collaborate across software, hardware, and algorithmic domains
- Develop algorithms reacting to real-time feedback
- Investigate new approaches to product signal capture
- Connect algorithm quality with real-world impact

Requirements:
- Computer science degree or equivalent engineering experience
- Systems thinking across the full machine learning stack
- Strong Python programming ability
- Experience with PyTorch, JAX, or TensorFlow
- Familiarity with RLHF and model fine-tuning

Bring cross-stack optimization, Python, and deep learning framework experience to systems shaping productive, continually adapting intelligence.

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📌 Machine Learning Systems Optimization Resident (Toronto)
🏢 Adaption
📍 Toronto