Job Summary:
Tesla is seeking a Machine Learning Engineer for their Digital Optimus project, where the role involves building training and evaluation systems for advanced computer-use agents. The engineer will focus on improving model performance and reliability through innovative training pipelines and evaluation frameworks.
Responsibilities:
• Build training pipelines that turn real agent interactions (screen data, actions, outcomes) into model improvements
• Design model architecture and training recipes to improve performance in a scientific way.
• Design evaluation frameworks and benchmarks to measure agent performance and identify failure modes
• Create data systems that extract high-quality signals from agent runs for continuous iteration
• Develop inference and model routing logic to combine reasoning and vision models efficiently in real time
• Ship end-to-end improvements that increase agent reliability and autonomy on long-horizon tasks
Qualifications:
Required:
• Strong applied ML fundamentals with a passion for turning research into robust, production-grade systems
• Experience designing model architectures and/or building training workflows, evaluation tools, data pipelines, or inference optimizations
• Solid software engineering skills and a high sense of ownership
• Background in agentic systems and/or multimodal (vision + language) models
• Excitement about making multimodal models reliable and effective in real-world agentic environments
Preferred:
• Experience with post-training or RL methods (PPO, GRPO, RLHF) is a strong plus
Company:
Tesla is an electric vehicle and clean energy company that provides electric cars, solar, and renewable energy solutions. Founded in 2003, the company is headquartered in Austin, USA, with a team of 10001+ employees. The company is currently Late Stage.