Job Description: Position Overview In this role, you will work on the research, training, optimization, and real-world deployment of embodied AI models, including Vision-Language-Action (VLA) models, World Models, and related robotics foundation models. You will work across multimodal perception, robot control, long-horizon task planning, large-scale robot data, and model deployment. This is a highly hands-on role that involves both model development and real-world robotic system integration. Key Responsibilities Embodied AI Model Development • Research and develop Vision-Language-Action (VLA), World Model, and other embodied AI foundation models. • Work on areas including: • Robotic manipulation • Multimodal perception and control • Long-horizon task planning • Vision-language-action reasoning • Robot-environment interaction • Develop and optimize models for real-world robotic applications. • Translate research ideas into practical models and systems that can operate reliably on physical robots. Foundation Model Training & Optimization • Train and optimize embodied AI foundation models using large-scale real-robot datasets and egocentric human demonstration data. • Design approaches for incorporating multimodal inputs such as: • Vision • Force • Tactile sensing • Proprioception • Other robot and environmental signals • Develop and evaluate multimodal fusion architectures. • Conduct model training, evaluation, benchmarking, and performance optimization. • Analyze model performance and identify opportunities to improve training efficiency, generalization, and real-world performance. Robotics Data Pipeline • Build and improve large-scale robotics data pipelines for model training. • Develop processes for: • Data cleaning • Data filtering • Resampling • Data augmentation • Data quality evaluation • Design scalable data pipelines capable of supporting large volumes of robot and human demonstration data. • Work closely with data and robotics teams to improve dataset quality and training efficiency. Model Deployment & Real-Robot Testing • Deploy trained models to physical robot systems. • Perform real-world robot debugging, testing, and performance optimization. • Diagnose issues across models, sensors, software, and robotic hardware. • Iterate between model training and real-world testing to improve system performance. • Help ensure models operate reliably and consistently in real-world environments. Qualifications Required • 1+ years of relevant industry or research experience in machine learning, robotics, computer vision, embodied AI, or a related field. • Strong understanding of deep learning and modern machine learning methods. • Experience with PyTorch or similar deep learning frameworks. • Experience training and evaluating machine learning models. • Strong programming skills in Python and familiarity with relevant ML/robotics tooling. • Understanding of multimodal learning, computer vision, robotics, or related areas. • Ability to work in a fast-paced startup environment and take ownership of technical problems from research through implementation. • Strong problem-solving and debugging skills. Strong Plus Real-World Robotics Deployment • Experience deploying and debugging machine learning models on physical robot systems. • Ability to bring models from development into real-world robotic environments. • Experience troubleshooting and stabilizing robotic systems in production or experimental environments. Multimodal / VLA Models • Experience working with force, tactile, or other multimodal sensing. • Experience designing or training multimodal fusion models. • Hands-on experience with VLA models, including model design, training, evaluation, or real-world applications. • Experience with robotic manipulation or embodied AI systems. Large-Scale Distributed Training • Experience with large-scale distributed model training. • Familiarity with DDP, DeepSpeed, FSDP, or similar distributed training frameworks. • Experience optimizing training performance, GPU utilization, memory usage, or training throughput. • Experience working with large-scale datasets and distributed data pipelines. Ideal Candidate We are looking for an engineer who is excited about the intersection of foundation models and physical robotics. The ideal candidate is: • Hands-on and comfortable moving between research, coding, experimentation, and real-world robot testing. • Interested in solving problems that cannot be addressed through simulation or software alone. • Comfortable working with large-scale datasets and modern foundation-model architectures. • Able to take ownership of a problem from data → training → evaluation → deployment → real-world iteration. • Comfortable working in an early-stage environment where priorities can move quickly. • Curious about emerging VLA, World Model, and embodied AI research and able to translate new ideas into working systems. Why Join MaxInsights? • Work directly on embodied AI and robotics foundation models. • Work with large-scale real-world robotics and human demonstration data. • Gain hands-on experience across the full AI development lifecycle, from data pipelines to real-robot deployment. • Work in a fast-moving startup environment with significant ownership and technical autonomy. • Collaborate with teams working at the forefront of robotics and foundation-model development. Default Benefits: • Health insurance • Vision care • Dental coverage • 401(k) • Paid holidays • PTO (Paid Time Off) • Sick leave