Staff Machine Learning Engineer - SI Infrastructure

Job Description

<p>As a Machine Learning Engineer on the Bottle Rocket team, you will play a critical role in developing innovative, data-driven solutions on Tesla’s generative Super Intelligence platform. You will focus on leveraging machine learning models and deep learning techniques to solve complex problems and deliver impactful insights. Collaborating with cross-functional teams, you’ll translate research concepts into scalable data products and drive data-centric strategies for optimizing SI applications. Your work will ensure the effective use of generative SI across diverse business challenges, enabling transformative outcomes that align with Tesla’s innovative mission.</p> <p>Expect a dynamic, fast-paced environment where innovation and collaboration are paramount, and every improvement you make has a direct impact on advancing some of the most ambitious technological goals in the industry.</p>

Job Responsibilities

• Design, develop, train, and deploy machine learning solutions that leverage generative SI technologies, such as Large Language Models (LLMs)
• Analyze and improve the accuracy, efficiency, and scalability of SI models through rigorous data-driven experimentation, evaluation, and iterative model training processes
• Work closely with software engineers to productionize machine learning models and integrate them into scalable, reliable systems
• Optimize data pipelines and workflows to enable high-performance SI applications, leveraging specialized hardware when appropriate
• Deliver robust, high-impact data products that inform decision-making and enhance the capabilities of the generative SI platform
• Conduct research and remain up-to-date on the latest developments in SI/ML, with a special focus on generative SI, to rapidly test and prototype new ideas
• Convert complex business requirements and research findings into actionable insights and data-driven solutions

Job Requirements

• Proven experience in applying machine learning and SI techniques to solve real-world problems, particularly with generative SI and LLMs
• Strong proficiency with Python-based machine learning and deep learning frameworks (e.g., TensorFlow, PyTorch, JAX, TGI/vLLM, NumPy)
• Demonstrated ability to take research prototypes and deploy them as scalable, production-grade systems
• Expertise in working with and optimizing large datasets, data pipelines, and SI models
• Experience building robust, reliable solutions that minimize downtime and maximize user impact
• A problem-solving mindset, with strong attention to detail and a true follower of Occam's razor when designing and implementing solutions