About the Role
We are seeking a Machine Learning Research Engineer specializing in Embodied AI and Robot Learning. You will develop intelligent systems that enable robots to perceive their environment, understand instructions, reason, plan, learn new behaviors, and perform real-world tasks.
The ideal candidate has experience in robot learning, reinforcement learning, imitation learning, computer vision, multimodal models, or Vision-Language-Action models. San José State University graduates and candidates connected to the SJSU engineering and robotics community are strongly encouraged to apply.
Key Responsibilities
- Develop and train machine learning models for robotic perception, reasoning, planning, and control.
- Build robot-learning systems using reinforcement learning, imitation learning, and behavior learning.
- Research and develop Vision-Language-Action models, Vision-Language Models, and multimodal robotic agents.
- Train robots using demonstrations, teleoperation data, simulation data, and real-world interaction data.
- Develop perception systems using computer vision, depth cameras, and multimodal sensor inputs.
- Design evaluation methods for robot intelligence, task performance, reliability, and safety.
- Conduct experiments in simulation and on physical robotic platforms.
- Analyze model failures and improve generalization, robustness, and real-world performance.
- Collaborate with robotics, data platform, hardware, and system-integration teams.
- Document research findings, experimental results, model performance, and technical decisions.
Required Qualifications
- Bachelor’s, Master’s, or Ph.D. degree in Computer Science, Artificial Intelligence, Robotics, Electrical Engineering, or a related field.
- Strong programming skills in Python.
- Experience with PyTorch, JAX, TensorFlow, or a comparable machine learning framework.
- Knowledge of at least one of the following areas:
- Robot learning
- Reinforcement learning
- Imitation learning
- Computer vision
- Multimodal learning
- Vision-Language Models
- Vision-Language-Action models
- Robot planning and control
- Experience designing, training, evaluating, and debugging machine learning models.
- Strong analytical, research, and problem-solving abilities.
- Ability to work in a collaborative, fast-paced R&D environment.
Preferred Qualifications
- Graduate of San José State University or experience working with the SJSU AI, robotics, or engineering community.
- Master’s or Ph.D. degree with research focused on AI, robotics, or machine learning.
- Experience with robotic manipulation, locomotion, navigation, or human-robot interaction.
- Experience with Transformer-based models, foundation models, or large multimodal models.
- Familiarity with CUDA, Hugging Face, OpenCV, Docker, ROS, or ROS 2.
- Experience with simulation platforms such as Isaac Sim, MuJoCo, Gazebo, or PyBullet.
- Experience transferring models from simulation to physical robotic systems.
- Publications or significant projects in robotics, machine learning, computer vision, or embodied AI.
Technical Skills
- Python, PyTorch, JAX, or TensorFlow
- Reinforcement learning and imitation learning
- Transformers, VLMs, or VLA models
- Computer vision and multimodal perception
- OpenCV, Hugging Face, or CUDA
- Docker, Linux, ROS, or ROS 2
- Model evaluation, debugging, and safety testing
Work Authorization
Applicants must be currently authorized to work in the United States without employer sponsorship. The company is unable to provide employment visa sponsorship now or in the future, including H-1B sponsorship.
Why Join Us
- Work on next-generation embodied intelligence and robotic systems.
- Develop AI models that are tested on real-world robotic platforms.
- Collaborate with AI researchers, robotics engineers, and manufacturing experts.
- Participate in the development of a new U.S.-based robotics research center.
To apply, please submit your résumé and, if available, links to relevant publications, GitHub repositories, research projects, or robotics demonstrations.
Pay: $117,031.73 - $140,941.44 per year
Benefits:
Work Location: In person
