Engineering Intern - ML Compilers (Spring 2027 Co-op)

Job description

Role Summary

Rivian internships are experiences optimized for student candidates. To be eligible, you must be an undergraduate or graduate student in an accredited program during the internship term with an expected graduation date between December 2027 and 2029. Rivian's Internship Program requires active student enrollment. Information regarding your expected degree completion date is collected solely to verify eligibility and determine your availability for future full-time opportunities. Rivian is an equal opportunity employer and does not use graduation dates to determine the age of applicants or as a basis for discriminatory hiring decisions.

If you are not pursuing a degree, please see our full time positions on our Rivian careers site. Note that if your university has specific requirements for internship programs, it is your responsibility to fulfill those requirements.

Our Spring Co-op program runs from Jan-Aug 2027. In order to be considered for this role, you must be available to work onsite, full time (40 hours per week), for the entire duration. If you're unable to work during the spring semester, please instead apply to our Summer Internship Program.

In this position you will be a key member of the ML Compiler team working on software tools to enable inference of deep learning networks hardware on Rivian Hardware Platforms. You will work closely with the Rivian Autonomy and Hardware teams and evaluate various implementation targeting for performance. You will help bring up new hardware and add support in the compiler for these hardware features.

This compiler enables HW-SW codesign and would result in developing efficient building blocks for state-of-the-art machine learning models. You will be collaborating with other cross functional teams in understanding the workloads, enabling running workloads on HW and help define the future enhancements to hardware and models.

Responsibilities

• Contribute to the development of an ML Compiler for mapping Autonomy ML models to Rivian Autonomy Processor (RAP1).
• Design and implement hardware-aware optimizations, including quantization strategies, model compression, memory-efficient representations, and operator fusion, targeted to RAP1.
• Collaborate with hardware teams to co-optimize model architecture and compute pipeline under real-time constraints (latency, throughput, power).
• Benchmark and analyze system performance across platforms and iterate to achieve optimal deployment efficiency.
• Partner with autonomy teams to align model optimization efforts with hardware roadmap and real-world autonomy requirements.

Qualifications

• Must be currently pursuing a masters or PhD degree at an accredited university
• Actively pursuing a degree or one closely related in Computer Engineering
• Excellent C/C++ and Python programming skills.
• Experience with various SOC platforms used for machine learning.
• Strong understanding of deep learning software models.
• Experience in compiler pipeline development preferred.
• Proficiency in deep learning frameworks and their low-level IRs or export formats.
• Solid programming skills in C++, Python

#LI-HH2

Pay Disclosure

The salary range for this role is $45.00-51.00 per hour for Palo Alto based applicants. This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, geographic location, shift, and organizational needs. We offer a comprehensive package of benefits including but not limited to paid vacation, paid sick leave, and medical insurance benefits. Internship positions are not eligible for retirement benefits. More information about benefits is available at rivianbenefits.com. You can apply for this role through careers.rivian.com (or through internal-careers-rivian.icims.com if you are a current employee). There is no fixed deadline for this application; applications are accepted on an ongoing basis until the role is filled or the opening is no longer needed.