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Mecka AI is building the data infrastructure layer for robotics and embodied AI. The company designs and operates global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.
You will advance the capabilities of large-scale vision-language and multimodal foundation models, working on challenging research problems spanning model training, post-training, reinforcement learning, and multimodal reasoning. This is a hands-on role for someone who enjoys experimenting with cutting-edge approaches, improving model performance, and translating research breakthroughs into real-world capabilities.
Key responsibilities include:
- Research, develop, and improve vision-language models and multimodal foundation-model architectures
- Design and implement advanced training strategies, including supervised fine-tuning, preference optimization, and reinforcement learning
- Investigate approaches to improve reasoning, generalization, reliability, and training efficiency
- Develop strategies for data curation, filtering, and synthetic data generation
- Design experiments, evaluation frameworks, and benchmarks to measure model performance and identify failure modes
- Work with distributed training infrastructure and large-scale datasets
- Explore emerging approaches in multimodal learning, model alignment, and foundation-model training
You will work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware, with opportunities across the full model development lifecycle.
REQUIREMENTS:
Must Have:
- Hands-on experience researching, training, fine-tuning, or post-training large-scale foundation models, particularly vision-language or multimodal models
- Experience working with Qwen, Qwen-VL, or comparable open-weight multimodal foundation models
- Strong understanding of deep learning architectures, optimization techniques, and modern model training methodologies
- Practical experience with supervised fine-tuning (SFT), preference optimization, or reinforcement learning approaches such as DPO, GRPO, PPO, or RLHF
- Strong proficiency in Python and PyTorch, JAX, or comparable deep learning frameworks
- Experience designing, executing, and analyzing large-scale machine learning experiments
- Familiarity with distributed training systems and GPU-based model development
- Strong research instincts, analytical problem-solving skills, and ability to independently drive complex technical projects
- Research publications, open-source contributions, or experience within leading AI research organizations
Nice to Have:
- Experience with large-scale model post-training and reinforcement learning for reasoning or alignment
- Familiarity with multimodal data pipelines involving images, video, or other sensory inputs
- Experience with reward modeling, synthetic data generation, or advanced model evaluation
- Experience improving model capabilities through innovations in training strategies, data quality, or model architecture
