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MaxInsights is seeking an Embodied AI / VLA Research Engineer to work on the research, training, optimization, and real-world deployment of embodied AI models, including Vision-Language-Action (VLA) models, World Models, and robotics foundation models. This is a highly hands-on role spanning multimodal perception, robot control, long-horizon task planning, large-scale robot data, and model deployment.
Key responsibilities include:
**Embodied AI Model Development**: Research and develop Vision-Language-Action (VLA), World Model, and other embodied AI foundation models. Work on robotic manipulation, multimodal perception and control, long-horizon task planning, vision-language-action reasoning, and robot-environment interaction. Develop and optimize models for real-world robotic applications, translating research ideas into practical systems that 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 including vision, force, tactile sensing, and proprioception. 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, filtering, resampling, augmentation, and 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. Ensure models operate reliably and consistently in real-world environments.
The ideal candidate is hands-on and comfortable moving between research, coding, experimentation, and real-world robot testing. They should be interested in solving problems that cannot be addressed through simulation or software alone, comfortable working with large-scale datasets and modern foundation-model architectures, and able to take ownership of problems from data through training, evaluation, deployment, and real-world iteration.
**Requirements**:
- 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
**Preferred Qualifications**:
- Real-world robotics deployment experience: deploying and debugging ML 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 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
