Head of AI Research

Summary

Join Standard Bots, a company revolutionizing real-world automation, as their Head of AI Research. In this role, you will develop and optimize AI models and training systems for cutting-edge AI/robotics applications. You will collaborate closely with the engineering team, design, implement, and iterate on large-scale AI models, and build efficient systems for rapid experimentation and deployment. The ideal candidate possesses extensive experience in ML planning within the autonomous vehicle space, utilizing advanced techniques in diffusion and autoregressive models. This position requires a proven track record of developing and deploying large-scale ML models and a strong understanding of modern ML architectures and training techniques. Standard Bots offers a competitive salary, stock options, and a comprehensive benefits package.

Requirements

• Have 5+ years of AI modeling experience, specifically within the self-driving car industry (or PhD with 3+ years of AI modeling experience in the self-driving car industry)
• Proven track record developing and deploying large-scale ML models
• Have experience using the latest techniques in diffusion and autoregressive models in a professional setting
• Are familiar with training inference and infra pipelines that go from camera input to trajectory output for self driving or robotics
• Have a strong understanding of modern ML architectures and training techniques
• Have experience with model debugging, optimization, and performance tuning
• PyTorch (experience with PyTorch required)
• Python
• NodeJS/Typescript
• Docker

Responsibilities

• Design and implement state of the art ML models and training pipelines
• Apply novel machine learning techniques to wide range of robotics applications
• Develop efficient data and training strategies
• Implement model evaluation frameworks and metrics tracking
• Lead model development and iteration with focus on
• Rapid experimentation and prototyping of new model architectures
• Performance optimization and model debugging
• Transfer learning and fine-tuning strategies
• Build robust evaluation and debugging systems to
• Analyze model behavior and failure modes
• Implement interpretability tools and visualization frameworks
• Track and improve model metrics
• Collaborate with engineering team to optimize training infrastructure and deployment

Preferred Qualifications

• Have experience with RL (reinforcement learning)
• Have a background in implementing ML research papers and adapting academic work

Benefits

• Paid time off
• Medical/dental/vision insurance
• Life insurance
• Disability insurance
• 401(k)
• Employee Stock Options