Embodied AI Engineer — Robotics

Innomium is moving into robotics, and we’re looking for an exceptional Embodied AI Engineer to join us.

We are building toward intelligent systems that can perceive, reason, learn, and act in the physical world. We’re looking for engineers and researchers with deep hands-on experience in Embodied AI, robotics, multimodal intelligence, simulation, data generation, benchmarking, and learning-based control.

This is not a traditional software engineering role. We are especially interested in people who have worked on systems where AI interacts directly with physical or simulated environments.

What You’ll Work On

You may work across areas including:

• Embodied AI and robotic intelligence
• Vision-Language-Action (VLA) models
• Robot learning and manipulation
• Reinforcement learning and imitation learning
• Learning from demonstrations and human feedback
• Multimodal perception and reasoning
• Vision-language models for robotics
• Navigation, planning, and control
• World models and spatial intelligence
• Sim-to-real and real-world robot deployment
• Robot foundation models
• Training and evaluating embodied agents
• Integration of AI models with robotic hardware and sensors
• Creating high-quality simulation environments for training and evaluation
• Designing new benchmarks and evaluation frameworks for embodied intelligence
• Generating synthetic, simulated, and real-world training data
• Building scalable data-generation pipelines for robotic learning
• Creating tasks, environments, scenarios, and evaluation suites to measure model capabilities
• Developing simulation infrastructure for large-scale robot training, testing, and experimentation

We value people who can move between research, experimentation, simulation, data, engineering, and real-world deployment.

What We’re Looking For

Strong candidates may have experience with some combination of:

• Embodied AI research or development
• Robotics and autonomous systems
• Manipulation, locomotion, or navigation
• Vision-Language-Action models
• Reinforcement learning, imitation learning, or behavior cloning
• Computer vision and multimodal models
• Transformer-based architectures and foundation models
• PyTorch or similar deep-learning frameworks
• ROS / ROS2 and robotics software stacks
• Robot simulation environments such as Isaac Sim, MuJoCo, Gazebo, Habitat, or similar platforms
• Building custom simulation environments and robotic tasks
• Generating synthetic datasets or large-scale robotic training data
• Designing benchmarks for manipulation, navigation, planning, reasoning, or general embodied intelligence
• Creating automated evaluation and testing pipelines
• Domain randomization, procedural environment generation, and sim-to-real transfer
• Training models using large-scale robotic or multimodal datasets
• Deploying learned policies on physical robots
• Sensors, cameras, depth perception, proprioception, and robot control

Academic credentials are welcome, but demonstrated ability and real experience matter more to us than titles.

How to Apply

Please send us a detailed description of your experience and capabilities in Embodied AI.

We would especially like to understand:

• What Embodied AI or robotics systems you have personally built or worked on
• Your exact contribution to each project
• The models, architectures, algorithms, and training approaches you used
• What robotic platforms, sensors, or simulation environments you have worked with
• Whether you have created your own simulation environments, tasks, or scenarios
• Whether you have designed benchmarks or evaluation frameworks for embodied agents
• How you have generated, collected, cleaned, labeled, or scaled training data for robotics or embodied AI
• Whether you have built synthetic-data or simulation-based data-generation pipelines
• Your experience with VLA models, robot foundation models, reinforcement learning, imitation learning, or related approaches
• Whether you have deployed AI models on real robots
• The most difficult technical problems you encountered and how you solved them
• Research papers, publications, GitHub repositories, demos, videos, datasets, benchmarks, or projects that demonstrate your work
• What you believe you can contribute to building advanced embodied intelligence at Innomium

Please be specific and technical.

Rather than simply listing technologies, we want to understand what you have actually built, what you personally accomplished, and the depth of your ability in Embodied AI.

If Embodied AI and the future of intelligent robotics are what you want to spend your time building, we’d like to hear from you.