Machine Learning Engineer, AI Infrastructure

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 AI 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 AI applications. Your work will ensure the effective use of generative AI across diverse business challenges, enabling transformative outcomes that align with Tesla's innovative mission. 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. Responsibilities Design, develop, train, and deploy machine learning solutions that leverage generative AI technologies, such as Large Language Models (LLMs) Analyze and improve the accuracy, efficiency, and scalability of AI 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 AI applications, leveraging specialized hardware when appropriate Deliver robust, high-impact data products that inform decision-making and enhance the capabilities of the generative AI platform Conduct research and remain up-to-date on the latest developments in AI/ML, with a special focus on generative AI, to rapidly test and prototype new ideas Convert complex business requirements and research findings into actionable insights and data-driven solutions Requirements Proven experience in applying machine learning and AI techniques to solve real-world problems, particularly with generative AI 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 AI 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 Compensation and Benefits Benefits Along with competitive pay, as a full-time Tesla employee, you are eligible for the following benefits at day 1 of hire: Aetna PPO and HSA plans > 2 medical plan options with $0 payroll deduction Family-building, fertility, adoption and surrogacy benefits Dental (including orthodontic coverage) and vision plans, both have options with a $0 paycheck contribution Company Paid (Health Savings Account) HSA Contribution when enrolled in the High Deductible Aetna medical plan with HSA Healthcare and Dependent Care Flexible Spending Accounts (FSA) LGBTQ care concierge services 401(k) with employer match, Employee Stock Purchase Plans, and other financial benefits Company paid Basic Life, AD&D, short-term and long-term disability insurance Employee Assistance Program Sick and Vacation time (Flex time for salary positions), and Paid Holidays Back-up childcare and parenting support resources Voluntary benefits to include: critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insurance Weight Loss and Tobacco Cessation Programs Tesla Babies program Commuter benefits Employee discounts and perks program Expected Compensation $140,000 - $300,000/annual salary cash and stock awards benefits Pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The total compensation package for this position may also include other elements dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.