Motional is hiring a
Principal Engineer

Summary

Join our team as a proactive, collaborative Principal Engineer for our ML training platform team. As the accountable owner of the ML training platform, you will lead a driven team to drive and deliver on key engineering objectives.

Requirements

• 6+ years of Python software development experience
• Hands-on experience with popular ML frameworks (PyTorch or TensorFlow)
• Hands-on experience with scaling ML systems
• Demonstrated problem solving skills and ability to think logically and remove bias from evaluation of problems
• Excellent programming and software design skills, including debugging, performance analysis, and test design
• Experience working on and leading large scale cross-functional projects
• Hands on experience with large cloud infrastructure. Strong cloud experience with AWS/GCP
• Comfortable working in a fast-paced, continuous delivery environment
• Strong understanding of machine learning approaches and algorithms
• Strong verbal and written communication skills to effectively work with various stakeholders, report insights, and rally support towards new initiatives and processes
• Proven track record of operating highly-available systems at scale
• Ability to proactively learn new concepts and technology and apply them at work
• Skilled at solving ambiguous problems
• Strong collaboration and mentorship skills

Responsibilities

• Design, build and maintain scalable ML data processing, model training solutions in the cloud infra
• Perform training and model performance optimization with various GPUs to improve model training speed and efficiency
• Execute program with a focus on technical execution to achieve program milestone
• Actively and proactively manage and mitigate technical risks
• Leads and continuously develops a high performing team that demonstrates Motional’s core values
• Inspire and motivate the team to work together as a cohesive and productive unit

Preferred Qualifications

• Hands-on experience with using Ray in the large scale environment
• Hands-on experience with ML data processing with large scale deep learning training
• Hands-on experience with refactoring ML code written by ML engineers