Universal Robots
AI Research Engineer
About this role
A Seattle-based startup is hiring an AI Research Engineer to develop foundational models for autonomous systems and robotics. You'll design ML experiments, implement large-scale models including transformers and diffusion architectures, and collaborate with a small research team to advance human-robot interaction.
What you'll do
- Design and iterate on ML pipelines for foundational model development
- Implement and train large-scale transformer and diffusion models for generative and control tasks
- Develop and evaluate reinforcement learning algorithms for autonomous behaviors
- Rapidly prototype research ideas into working code
- Integrate ML components into real-world systems with cross-functional teams
- Stay current with AI research and share insights internally and externally
What they're looking for
- Large-scale model training (transformers, diffusion models)
- Reinforcement learning fundamentals and applications
- Python and ML frameworks (PyTorch, JAX, TensorFlow)
- Writing clean, scalable, efficient code
- ML experimentation and evaluation
- Collaboration and communication
- Simulation environments or robotics systems (preferred)
- Multimodal architectures and imitation learning (preferred)
Benefits
- Relocation stipend for candidates moving to Seattle from 50+ miles away
- Work on cutting-edge AI and robotics technology
- Collaborate with a small, dynamic team of AI researchers
- Influence the future of human-robot interaction
- Equal opportunity employer with disability accommodations
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Universal Robots
Collaborative Robotics develops foundational AI models and autonomous systems focused on advancing human-robot interaction. The company is hiring AI Research Engineers to design and implement large-scale machine learning models, including transformers and diffusion architectures, that power their robotics platform.
- Website
- universal-robots.com
Likely interview questions
- Walk us through a project where you trained a large-scale model like a transformer or diffusion model from scratch. What challenges did you face and how did you optimize the training pipeline?
- Describe your hands-on experience with reinforcement learning. Have you implemented and debugged RL algorithms in a real application, and how did you approach evaluation?