AI Research Engineer

Sourced from feeny.ai

Cobot is hiring an AI Research Engineer for its Foundational Models AI team in Seattle to advance machine learning research for human-robot interaction. The role blends ML research and software engineering, turning ideas into working prototypes across reinforcement learning, transformers, and diffusion models. You will design and iterate on experiments that shape foundational models for autonomous systems and intelligent robotics.

• Design, build, and iterate on experimental ML pipelines supporting foundational model development
• Implement and train large-scale models, including transformers and diffusion-based architectures, for generative and control tasks
• Develop and evaluate reinforcement learning algorithms and frameworks for autonomous behaviors
• Rapidly prototype and deploy research ideas into working code to accelerate AI experimentation cycles
• Collaborate with cross-functional teams to integrate ML components into real-world systems
• Stay current with the latest AI research and share insights internally and externally

• Master's degree in Machine Learning, AI, Computer Science, or a related technical field
• Minimum 3 years of experience in applied ML research and software engineering
• Hands-on experience training (not just using) large-scale ML models such as transformers or diffusion models
• Strong understanding of reinforcement learning fundamentals and applications
• Proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow
• Proven ability to write clean, efficient, and scalable code that supports fast iteration
• Excellent collaboration and communication skills
• Willing to occasionally travel
• Must have and maintain US work authorization

• PhD in Machine Learning, AI, Computer Science, or related field
• 4+ years of experience in applied ML research and software engineering
• Experience designing ML experimentation frameworks and model training pipelines
• Practical knowledge of simulation environments or robotics systems
• Familiarity with multimodal architectures and imitation learning
• Understanding of edge compute or distributed ML training infrastructure