Embodied AI / VLA Research Engineer

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