About Us:
Rivian and Volkswagen Group Technologies is a joint venture between two industry leaders with a clear vision for automotive’s next chapter. From operating systems to zonal controllers to cloud and connectivity solutions, we’re addressing the challenges of electric vehicles through technology that will set the standards for software-defined vehicles around the world.
The road to the future is uncharted. By combining our expertise across connectivity, AI, security and more, we’ll map a new way forward. Working together, we’ll create a future that’s more connected, more intelligent, more sustainable for everyone.
Role Summary:
At Rivian, our mission revolves around developing vehicles powered by software and data. As a Staff Machine Learning Engineer, you will be a crucial part of our team, working alongside engineers, data scientists, and domain experts to build models at scale, predict vehicle performance, and optimize SW parameters throughout the vehicle’s lifetime. We leverage the power of big data collected from our extensive customer and commercial fleets to achieve this.
We are looking for a Staff Machine Learning Engineer specializing in Deep Learning to build cutting-edge models for vehicle intelligence, anomaly detection, and predictive maintenance. If you thrive in a research-driven, applied AI environment, this is the perfect opportunity to push the boundaries of GANs, VAEs, Transformers, and Autoencoders in real-world applications.

Responsibilities:

• Develop and optimize RL-based models for vehicle control, smart scheduling, and dynamic decision-making.
• Apply Imitation Learning to enhance vehicle behavior prediction and self-adaptive AI systems.
• Architect Generative AI models (GANs, VAEs, Transformers, Autoencoders) for anomaly detection, predictive maintenance, and real-time vehicle intelligence.
• Build self-learning AI systems that improve vehicle performance over time through continuous learning from fleet data.
• Design unsupervised & self-supervised learning models to process high-dimensional vehicle telemetry data.
• Collaborate with data scientists, software engineers, and vehicle system experts to deploy AI-powered automotive solutions.
• Work on LLM-based automation tools, including AI-driven requirement management and chatbot-based vehicle control support.
• Improve MLOps pipelines for efficient AI/ML deployment and lifecycle management.

Qualifications:

• M.S. or Ph.D. in Machine Learning, AI, Computer Science, or a related STEM discipline.
• Expertise in Reinforcement Learning (RL, RLHF, RLAIF) and Imitation Learning.
• Experience developing Generative AI models (GANs, VAEs, Transformers, Autoencoders).
• Proficiency in deep learning frameworks (TensorFlow, PyTorch).
• Strong software engineering skills in Python, Apache Spark, and cloud-based ML frameworks (AWS/GCP/Azure).
• Experience optimizing AI models for real-time decision-making and large-scale deployment.

Pay Disclosure:
Salary Range/Hourly Rate for Palo Alto, California Based Applicants: $146,900 - 183,600 USD (actual compensation will be determined based on experience, location, and other factors permitted by law).
Salary Range/Hourly Rate for California Based Applicants: $135,100 - 183,600 USD (actual compensation will be determined based on experience, location, and other factors permitted by law).
Benefits Summary: Rivian and Volkswagen Group Technologies provides robust medical/Rx, dental and vision insurance packages for full-time employees, their spouse or domestic partner, and children up to age 26. Coverage is effective on the first day of employment.
Company Statements:

• Develop and optimize RL-based models for vehicle control, smart scheduling, and dynamic decision-making.
• Apply Imitation Learning to enhance vehicle behavior prediction and self-adaptive AI systems.
• Architect Generative AI models (GANs, VAEs, Transformers, Autoencoders) for anomaly detection, predictive maintenance, and real-time vehicle intelligence.
• Build self-learning AI systems that improve vehicle performance over time through continuous learning from fleet data.
• Design unsupervised & self-supervised learning models to process high-dimensional vehicle telemetry data.
• Collaborate with data scientists, software engineers, and vehicle system experts to deploy AI-powered automotive solutions.
• Work on LLM-based automation tools, including AI-driven requirement management and chatbot-based vehicle control support.
• Improve MLOps pipelines for efficient AI/ML deployment and lifecycle management