AI Engineer

Responsibilities

• Design, train, and evaluate multimodal foundation models that enable multi-robot mission execution
• Drive model development decisions including model architectures, data mixtures, curriculum design, and training recipes informed by rigorous experimentation
• Curate and generate large-scale training datasets from both synthetic and real-world data engines
• Develop memory, communication, planning, and tool-use capabilities for AI agents operating across teams of robots
• Be obsessed with evaluation: design benchmarks, metrics, and testing methodologies that enable rapid iteration across the team
• Build and improve simulation environments targeted for synthetic data generation, evaluation, and reinforcement learning
• Translate foundational research into deployable, real-time perception and decision-making systems
• Collaborate across engineering, robotics, and mission teams to integrate AI systems with onboard autonomy stacks
• Conduct field trials and mission operations to validate system performance under real-world constraints

Qualifications

• 2+ years of hands-on experience building and deploying AI models, including 2+ years working with multimodal models or agentic AI systems
• Proficiency in Python, PyTorch, and modern machine learning infrastructure
• Experience training, finetuning, and evaluating large-scale deep learning models using distributed compute infrastructure
• Demonstrated experience improving model performance through data curation, training strategy, evaluation design, and rigorous experimentation
• Solid grasp of modern model training techniques including SFT, DPO, RLVR
• Demonstrated ability to take research from prototype to product in fast-moving environments
• BS, MS, or PhD in Computer Science, Engineering, Mathematics, Physics, or related technical field, or equivalent practical experience
• Advanced graduate research in AI, robotics, or machine learning is a plus
• Bonus: Experience building closed-loop simulation, evaluation, or reinforcement learning environments for agentic or physical AI systems