Bring AI to the Physical World!
about the company
We are representing a well-funded, hyper-growth deep-tech startup building the next generation of AI for robotics. Theor mission is to revolutionize industrial automation by creating intelligent, adaptable software that controls complex physical systems. If you want to see your code move real-world machinery in real-time, this is the place for you!
about the role
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As an Applied AI Scientist (Robotics), you will bridge the gap between AI models and real-world physical motion. You will work in a fast-paced environment, iterating daily to bring virtual models into reality on physical hardware.
- Model Development: Design and optimize machine learning models for robotic movement and dexterity (utilizing reinforcement learning and behavior cloning) and deploy them onto physical machines.
- AI Infrastructure: Manage the end-to-end AI lifecycle. This includes handling sensor data, maintaining GPU training workflows, and tracking large-scale experiments.
- Sim-to-Real Deployment: Gather real-world data, troubleshoot edge cases, and seamlessly transfer models from virtual simulations to the physical world.
- Tooling & Collaboration: Build internal tools for data analysis and work closely with hardware and systems engineering teams to deliver field-ready automation.
skills and experience
- Education & Experience: Degree in Computer Science, AI, Robotics, or a related field. An advanced degree (MSc/PhD) is a strong plus, but solid industry experience is just as valuable.
- Technical Stack: Strong programming skills and deep experience with modern deep learning frameworks, specifically PyTorch.
- Robotics & AI: Proven experience with agent-based training, policy learning, and neural control systems.
- Sim-to-Real Execution: A track record of taking algorithms from simulated environments and successfully deploying them onto real-world, functional hardware.
Preferred Bonus Skills:
- Experience with large multimodal foundation models for physical agents (e.g., vision-language-action models) or generative video models.
- Previous contributions or publications in top-tier AI/Robotics conferences (ICRA, IROS, NeurIPS, CVPR, or equivalent).
To apply online please use the 'apply' function, alternatively you may contact Evangeline. (EA: 94C3609/ R24124002)
