SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Mecka AI is building the data infrastructure layer for robotics and embodied AI, designing and operating global systems for data capture, labeling, and hardware-enabled workflows used by leading AI labs and robotics companies. The company works closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware.
You will advance the capabilities of large-scale vision-language and multimodal foundation models. This is a hands-on research role focused on experimenting with cutting-edge approaches, improving model performance, and translating research breakthroughs into real-world capabilities. You'll work across the model development lifecycle, from data and training strategies to evaluation and optimization.
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
Requirements:
- 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
