Embodied AI / VLA Research Engineer

About this role

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

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401(k)

• Paid holidays

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PTO (Paid Time Off)

• Sick leave