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Salary: USD 250,000 - 300,000 / annual
Standard Bots is revolutionizing real-world automation by making robotic systems accessible through AI-powered platforms. The company enables robots to tackle unprecedented challenges via an intuitive instruction interface, bringing software automation capabilities to physical spaces.
As a Staff AI Research Engineer, you will lead the development and optimization of AI models and training systems for cutting-edge AI/robotics applications. You'll work closely with a small, focused engineering team to design, implement, and iterate on large-scale AI models while building efficient systems for rapid experimentation and deployment. This role requires working across the full stack—from data pipelines to model architecture to deployment infrastructure—to solve real customer problems.
Key responsibilities include:
- Designing and implementing state-of-the-art ML models and training pipelines, applying novel machine learning techniques to robotics applications, developing efficient data and training strategies, and implementing model evaluation frameworks
- Leading model development with focus on rapid experimentation, performance optimization, model debugging, transfer learning, and fine-tuning strategies
- Building robust evaluation and debugging systems to analyze model behavior, implement interpretability tools, and track improvements
- Collaborating with the engineering team to optimize training infrastructure and deployment pipelines
You should have 7+ years of AI modeling experience specifically in the self-driving car industry (or a PhD with 3+ years in that domain). Required expertise includes proven track record deploying large-scale ML models, hands-on experience with diffusion and autoregressive models in production, familiarity with end-to-end training/inference pipelines from camera input to trajectory output, reinforcement learning experience, and strong understanding of modern ML architectures. Experience implementing ML research papers and adapting academic work is essential. PyTorch proficiency is required; Python, NodeJS/Typescript, and Docker experience expected.
