Internship - Software Engineer, AI Infrastructure (Fall 2026)

Position Overview

This internship role begins in August or September 2026 and generally lasts through the summer term, concluding around December 2026, with a possibility to extend into Winter/Spring 2027 based on availability and opportunity. Candidates should commit to at least 12 weeks of full-time work (40 hours/week) onsite. This program is designed exclusively for students enrolled in academic programs; recent graduates seeking full-time employment should apply to full-time positions rather than internships.

International students with CPT work authorization should verify with their institution whether they can commit to 40 hours per week on-site prior to applying, as many may have part-time limitations during academic terms.

Role Description

The intern will contribute to scaling state-of-the-art large AI models supporting Tesla’s Autopilot, Optimus, and Digital Optimus projects. This position intersects distributed systems engineering, machine learning, and performance optimization, working closely with machine learning practitioners and infrastructure engineers to enhance training efficiency, accelerate experimentation cycles, and enable development of more powerful models.

Ideal candidates will be passionate about both systems engineering and machine learning, able to debug distributed training challenges, interpret model behaviors, and utilize data to link infrastructure enhancements with model performance improvements.

Key Responsibilities

• Optimize distributed training across thousands of GPUs at large scale
• Enhance training throughput, resource utilization, reliability, and scalability
• Develop diagnostic tools to pinpoint bottlenecks in computing, networking, memory, and data workflows
• Implement performance enhancements within PyTorch, CUDA, communication protocols, and training frameworks
• Partner with researchers to assess how infrastructure changes influence model quality, convergence, and related metrics
• Create dashboards correlating system performance with model outcomes
• Drive efficiencies in model scaling, including accommodating larger models, extended contexts, and improved datasets
• Troubleshoot complex issues spanning software, hardware, networking, and ML systems
• Build infrastructure to accelerate experimentation and shorten iteration time for researchers

Candidate Qualifications

• Solid foundation in software engineering using Python and C++
• Experience or knowledge in distributed computing, high-performance systems, or large-scale infrastructure
• Familiarity with machine learning core concepts, including optimization, training processes, and evaluation methods
• Working knowledge of PyTorch and contemporary deep learning frameworks
• Proficiency in identifying performance bottlenecks with profiling and observability tools
• Strong analytical and debugging capabilities
• Effective communication and teamwork skills
• Currently pursuing a degree in Computer Science or a related discipline, with expected graduation between December 2026 and December 2027

Compensation and Benefits

The anticipated annual salary range for this full-time internship is $100,000 to $150,000, supplemented by an extensive benefits package including:

• Medical insurance options including plans with no payroll deductions
• Fertility, adoption, surrogacy, and family-building support
• Dental and vision coverage, with some plans requiring no payroll contribution
• Company contributions to Health Savings Account (HSA) with eligible high deductible plans
• Flexible Spending Accounts for healthcare and dependent care
• Retirement plans such as 401(k) and employee stock purchase programs
• Basic life, accidental death & dismemberment, and short-term disability insurance
• Employee Assistance Program services
• Sick leave, vacation time, and paid holidays
• Back-up childcare and parenting resources
• Voluntary benefits including critical illness, accident, hospital indemnity, legal, theft, and pet insurance
• Commuter benefits and employee discounts/perks program

Compensation is influenced by factors including location, relevant expertise, skills, and experience. Additional details will be provided upon offer of internship.