Machine Learning Engineer

Machine Learning Engineer, Reliability Engineering

Job Category: Location

Req. ID: 238827

Job Type: Full-time

What to Expect:

Tesla is looking for an exceptional Machine Learning Engineer to build predictive life models to drive the future of intelligent Tesla products.

You will collect some of the largest datasets of Real Time test, factory and fleet of Vehicle & Energy products and develop machine learning models to unlock decision making on prognostics and targeted design actions. Your work will impact and be utilized by millions of Tesla's customers.

What You'll Do:

1. Design, develop, train, and deploy predictive/control models of physical degradation, usage and system performance.
2. Build robust, flexible and automated software tools to enable complex analysis of Real Time fleet.
3. Design scalable and reliable data pipelines to productionize and monitor both new and existing models.
4. Convert complex business requirements and research findings into actionable insights and data-driven solutions.
5. Conduct research and remain up to date on the latest developments in AI/ML, with a special focus on physics-informed AI, to rapidly test and prototype new ideas.

What You'll Bring:

1. Proficiency in writing production-quality code in Python; experience with major deep learning frameworks, and software engineering best practices.
2. Practical experience with C to help integrate with firmware and take project ideas to shipped products.
3. Expertise in working with and optimizing large datasets, data pipelines, and AI models.
4. Solid understanding of linear algebra, probabilistic theory, numerical optimization, and deep learning, with hands-on implementation experience.
5. General knowledge of physics and engineering principles.
6. Nice to have: Record of coming up with new ideas (or improving upon existing ideas) in statistical modeling or machine learning, demonstrated by accomplishments such as first author publications or open-source projects.

Compensation and 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).
• 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:

$111,200 - $200,160/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.

Tesla is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to any factor, including veteran status and disability status, protected by applicable federal, state or local laws.

Tesla is also committed to working with and providing reasonable accommodations to individuals with disabilities. Please let your recruiter know if you need an accommodation at any point during the interview process.

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