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
- Strong 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 in Richmond Hill employer: Mecka
At Mecka, we pride ourselves on fostering a collaborative and innovative work culture that empowers our employees to take ownership of their projects. As an End-to-End Camera & Sensor Calibration Engineer, you will have access to cutting-edge technology and the opportunity for professional growth in a dynamic environment. Our commitment to quality and teamwork ensures that you will be part of a supportive community dedicated to excellence in production systems.
