Sr/Staff Machine Learning Engineer, Planning, Autonomous Driving

About the Role

Key Responsibilities

• Design and refine behavioral models using advanced deep learning techniques to ensure safe and efficient autonomous driving and to navigate complex driving scenarios effectively.
• Train and integrate deep learning models with real-world data and simulation frameworks to enhance model accuracy and reliability.
• Collaborate with researchers and engineers to push the boundaries of AI, driving innovation in autonomous vehicle technology.
• Requires verbal and written communication skills to convey complex information.
• May require negotiation, influence, tact, etc.
• Has a great degree of influence over key organizational decisions.
• Tasks do not have defined steps; planning, problem-solving, and prioritization must occur to complete the tasks effectively.
• Provides leadership and supervision/guidance to other team members.

Requirements

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.
• OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Systems Engineering or related work experience.
• OR PhD in Engineering, Information Systems, Computer Science, or related field.
• Strong proficiency in Python, with hands-on experience using machine learning frameworks such as PyTorch.
• Solid understanding of deep learning, including the underlying principles and mechanisms of modern deep learning algorithms.
• Proven ability to design, adapt, and implement advanced machine learning algorithms.
• Excellent communication skills and a collaborative mindset for working in cross-functional teams.

Nice to Have

• Ph.D. in a relevant field with at least 1 year of industry experience in autonomous driving or robotics.
• Expertise in deep learning and generative models, including transformers and diffusion-based architectures.
• Practical experience in reinforcement learning, including offline RL, reward modeling, and related techniques.
• Experience working with both simulated environments and real-world datasets for model validation and benchmarking.
• A strong publication record in top-tier conferences such as NeurIPS, CVPR, ICRA, ICLR, or CoRL.
• Familiarity with autonomous systems, sensor data processing, and real-time decision-making algorithms.
• Experience with large-scale ML systems, distributed training, and deploying models in production environments.

Qualifications

• Bachelor's, Master's, or PhD in Engineering, Information Systems, Computer Science, or related field.

Benefits & Perks

• Competitive salary ranging from $111,300.00 to $166,900.00.
• Annual discretionary bonus program.
• Opportunity for annual RSU grants.
• Highly competitive benefits package supporting success at work, home, and play.