sensmore is a Berlin/Potsdam-based robotics startup delivering production-proven automation for industries where the world’s raw materials are extracted, moved, and processed. Its automation system transforms heavy machines into intelligent, automated robots powered by Physical AI and vertically integrates them into the full production environment: from the machine and safety infrastructure to network infrastructure, site processes, and operational interfaces.

Co-developed with customers, sensmore is backed by Point Nine Capital, leading industry investors, the State of Brandenburg, and the European Union.

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.

Key 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