Senior Machine Learning Engineer

What you'll Accomplish:

• Research, prototype, and develop AI-driven models for robotic manipulation, grasping, and object interaction.
• Design and optimize Classical ML / deep learning and reinforcement learning (RL) models for robotic control and decision-making.
• Develop simulation-driven ML pipelines, integrating synthetic data, real-world sensor data, and reinforcement learning.
• Work with different learning models, including Large Language Models (LLMs), transformer-based architectures, and generative AI to enhance robotic intelligence.
• Collaborate with robotics engineers, software developers, and mechatronics teams to integrate ML models into real-world robotic systems.
• Design and implement scalable ML frameworks for real-time robotics applications.
• Conduct model evaluation, benchmarking, and performance tuning to improve accuracy, robustness, and generalization.
• Develop and maintain data pipelines, including sensor data collection, annotation, and preprocessing for AI model training.
• Experiment with foundation models and generative AI for robotics applications.
• Work with physics-based simulators (Isaac Sim, Mujoco, PyBullet, Gazebo, DRAKE) to train and validate ML models.

Who you are:

• 5+ years of experience in machine learning, deep learning, or reinforcement learning for robotics.
• Hands-on experience with robotic perception, motion control, and adaptive decision-making models.
• Strong background in ML model development, training pipelines, and real-world deployment.
• Experience in R&D environments, rapidly prototyping and iterating on AI models.
• Familiarity with robotics simulation environments and synthetic data generation.
• Strong understanding of multi-modal sensor fusion (LiDAR, depth cameras, IMUs) and time-series modeling.
• Experience working with different AI models, including LLMs, generative AI, diffusion models, and reinforcement learning algorithms.
• Programming: Proficiency in Python, C++, and experience with robotics frameworks (ROS2, PyTorch, TensorFlow, JAX, OpenCV).ML Techniques: Deep learning (CNNs, Transformers), reinforcement learning (PPO, SAC, DDPG), and self-supervised learning.
• Simulation & Modeling: Experience with physics-based simulation tools (Isaac Sim, Mujoco, Gazebo, PyBullet).Data Processing: Expertise in large-scale dataset curation, sensor fusion, and distributed ML pipelines.
• Optimization & Deployment: GPU acceleration, CUDA, TensorRT, and real-time model optimization.
• Experience with edge AI and deploying ML models on embedded robotics platforms.
• Background in multi-agent reinforcement learning (MARL) for robotic coordination.
• Understanding of generative AI (Diffusion Models, Transformers) applied to robotic environments.
• Experience with foundation models and fine-tuning large-scale pretrained ML models for robotics.
• Passionate about AI for robotics and solving hard, real-world problems.
• Strong R&D mindset, excited about prototyping and pushing AI innovation.
• Self-driven, curious, and thrives in an experimental, fast-paced environment.
• Effective collaborator, able to work cross-functionally with robotics and software teams.
• Willing to work onsite at our Cambridge,MA office atleast 3 days a week