Embodied AI / VLA Research Engineer

Maxinsights•Santa Clara, CA

About The Position

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.

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.

Nice To Haves

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.
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.
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.
Excited about the intersection of foundation models and physical robotics.
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.

Responsibilities

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.
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.
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.
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.