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Salary: USD 225,000 - 250,000 / annual
Standard Bots is revolutionizing real-world automation by making robotic systems accessible through an AI-powered platform that enables robots to tackle unprecedented challenges via an intuitive instruction interface.
You will develop and optimize AI models and training systems for cutting-edge AI/robotics applications. Working closely with a small team of AI engineers, you'll design, implement, and iterate on large-scale AI models while building efficient systems for rapid experimentation and deployment. This role requires working across the stack to deliver models that solve real customer problems.
Key responsibilities:
- Design and implement 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 and metrics tracking.
- Lead model development and iteration with focus on rapid experimentation and prototyping of new model architectures, performance optimization and model debugging, and 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, and track and improve model metrics.
- Collaborate with the engineering team to optimize training infrastructure and deployment.
Tech stack: PyTorch (required), Python, NodeJS/Typescript, Docker.
REQUIREMENTS:
- 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
- Experience using the latest techniques in diffusion and autoregressive models in a professional setting
- Familiarity with training, inference, and infrastructure pipelines that go from camera input to trajectory output for self-driving or robotics
- Experience with reinforcement learning (RL)
- Strong understanding of modern ML architectures and training techniques
- Experience with model debugging, optimization, and performance tuning
- Background in implementing ML research papers and adapting academic work
