AI Engineer - Foundational Vision & Language Models for Robotics

Mid–Senior | Robotics | Applied Research & Deployment

We are hiring a Mid–Senior AI Engineer to join our AI team and work on large language, vision-language, and vision-language-action models for robotic autonomy. This role will focus on improving the robot’s reasoning, planning, and autonomous decision-making capabilities using modern foundational AI models.

You will collaborate closely with the AI, robotics, and embedded software teams to build intelligent robotic capabilities end to end — from model development and evaluation to integration and deployment on real robots with onboard compute.

What You’ll Do

• Design, develop, and deploy LLM-, VLM-, and VLA-based systems for robotic reasoning, planning, perception, and action.
• Build agentic and multi-agent systems for robotic decision-making and task execution.
• Develop human–robot interaction capabilities using language, vision, and multimodal inputs.
• Fine-tune, adapt, evaluate, and deploy foundation models for real-world robotic use cases.
• Integrate AI models with perception, navigation, control, and ROS-based robotic systems.
• Work on model deployment for fully onboard and hybrid edge/cloud compute scenarios.
• Build evaluation pipelines for robot autonomy, model behavior, task success, reliability, and safety.
• Collaborate with embedded software and robotics engineers to connect high-level AI systems with real-time robot execution.
• Translate research ideas into practical, robust systems that operate on physical robots.

Requirements

• Strong background in Python, machine learning, deep learning, and applied AI development.
• Hands-on experience working with LLMs, VLMs, multimodal models, or foundation model pipelines.
• Experience with model fine-tuning, prompting, evaluation, inference optimization, or deployment.
• Familiarity with agent frameworks, tool-using models, planning systems, or similar architectures.
• Strong understanding of modern AI model development workflows, including data preparation, training, evaluation, and deployment.
• Ability to work across software boundaries, from research code to production-quality robotic systems.
• Strong systems thinking and the ability to reason about end-to-end autonomy pipelines.
• Experience debugging complex AI systems in real-world or simulated environments.