PhD Research Internship - Robotics Engineer (VLM / VLA Models)

Responsibilities

• Depending on your expertise and project priorities, you will:
• Research & Method Development
• Design and develop novel approaches for Vision-Language-Action systems in real-world industrial settings
• Explore scalable architectures for multi-modal reasoning and action generation
• Contribute to advancing state-of-the-art methods in embodied AI and robotic autonomy
• Multi-Modal Learning & Data Systems
• Lead the design and analysis of large-scale multi-modal datasets (video, radar, lidar, sensor fusion)
• Develop self-supervised or weakly supervised dataset generation pipelines for VLA training
• Investigate data-centric approaches to improve robustness and generalization
• Model Development & Optimization
• Build, adapt, and extend cutting-edge GenAI models (e.g., VLMs, VLA frameworks)
• Apply advanced fine-tuning strategies (e.g., parameter-efficient tuning, alignment methods)
• Explore prompt optimization, reasoning augmentation, and action grounding techniques
• Training, Evaluation & Benchmarking
• Design rigorous evaluation protocols for embodied AI systems in industrial contexts
• Run large-scale experiments, analyze performance, and iterate systematically
• Benchmark models against state-of-the-art approaches and internal baselines
• Deployment & Systems Integration
• Collaborate with engineering teams to transition research prototypes into production-ready systems
• Optimize models for real-time inference, robustness, and safety in heavy-industry environments
• Scientific Contribution
• Document findings and contribute to research publications, technical reports, or patents
• Present results internally and potentially at leading conferences
• Required Qualifications
• Current enrollment in a PhD program in Robotics, Computer Science, Machine Learning, Electrical Engineering, or a related field
• Strong programming skills in Python and deep learning frameworks (e.g., PyTorch)
• Solid understanding of machine learning, deep learning, and multi-modal models
• Proven ability to conduct independent research and drive projects from idea to results
• Strong analytical thinking and problem-solving skills
• Preferred Skills & Experience
• Experience with Vision-Language Models , embodied AI , or robotics learning systems
• Familiarity with modern GenAI tooling (e.g., Hugging Face ecosystem, Gemini, Unsloth, or similar)
• Experience with multi-modal data (vision + sensor fusion)
• Background in robotics, control systems, or real-world deployment
• Track record of research output (publications, preprints, or significant research projects)
• Experience with large-scale training, distributed systems, or model optimization
• Research Environment & Outlook
• Opportunity to work on high-impact, real-world robotics problems at the intersection of AI and industrial automation
• Collaboration with a multidisciplinary team spanning AI research and robotics engineering
• Potential to publish and contribute to the scientific community
• Opportunity to shape long-term research directions and transition work into real-world deployment

Benefits

Additional Information

sensmore automates the world's largest machines with unprecedented intelligence. Our proprietary Physical AI enables heavy machines such as wheel loaders to instantly adapt to dynamic environments and execute new tasks without prior training. We integrate cutting-edge robotics into a platform powering intelligence and automation products - transforming productivity and safety for customers in mining, construction, and adjacent industries today. Join us and play a pivotal role in transforming the automation landscape in heavy industries. Role Overview We are seeking a highly motivated PhD candidate to join our team as a Research Intern specializing in General Purpose AI, with a focus on Vision-Language Models and Vision-Language-Action systems. This role sits at the frontier of industrial robotics: developing scalable, general-purpose VLA systems that enable robots to perceive, reason, and act autonomously in complex heavy-industry environments. You will contribute to bridging multi-modal perception (e.g., video, radar, lidar) with robust real-world execution, while advancing state-of-the-art methods in embodied AI. Beyond engineering, this position has a strong research component , with opportunities to contribute to novel methods, publish findings, and shape the future of industrial autonomy.

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