Advance vision-language models at Mecka AI through multimodal research, post-training, and reinforcement learning for embodied AI systems.

This experienced Research Scientist will develop large-scale multimodal foundation models across their development lifecycle. You will research architectures, training strategies, evaluation methods, and optimization techniques. The work connects data quality, model reasoning, reliability, and real-world capabilities.

Key Responsibilities:

- Research and improve vision-language model architectures
- Implement supervised fine-tuning, preference optimization, and reinforcement learning
- Investigate reasoning, generalization, reliability, and training efficiency
- Design evaluations and benchmarks to identify model failure modes




- Run experiments with distributed infrastructure and large datasets

Requirements:

- Hands-on experience training large-scale multimodal foundation models
- Experience with Qwen, Qwen-VL, or comparable models
- Robust understanding of deep learning and optimization
- Proficiency in Python and PyTorch, JAX, or comparable frameworks
- Experience analyzing large-scale machine learning experiments

Bring multimodal model research, reinforcement learning, and evaluation expertise to Mecka AI’s embodied AI systems.

📌 Multimodal Foundation Model Scientist (Richmond Hill)
🏢 mecka
📍 Richmond Hill