ML Research Intern — Driver Refinement Foundations

Waymo LLC; San Francisco (CA); On-site; USD 96,000 - 117,000; Full time

Benefits offered by this job

Housing/relocation bonus (if eligible)

Medical, dental, and vision insurance

Fun intern events and networking

Free breakfast, lunch, dinner, and Sn…

Free access to Google shuttles

Onsite gym

Job summary

Waymo LLC in San Francisco is seeking a graduate student intern or entry-role software engineer focused on data and model development for the Waymo Driver platform. You will mine and curate driving data, and apply language models and foundation models for reasoning, perception, and decision making.

You will design data generation pipelines, run experiments for model training, and contribute to robust software engineering practices.

Qualifications

• Graduate student actively enrolled in a Master’s or PhD program in computer science or a related technical field.
• Strong problem solving and programming skills in Python and/or C++.
• Good understanding of Jax/PyTorch.
• Experience in model evaluation and understanding metrics.
• Experience in solid software engineering practices.
• PhD preferred; internship experience welcomed.

Responsibilities

• Mine and curate dataset samples from a large corpus of driving data.
• Leverage large language models and foundation models for reasoning, decision making and multi-modality generation.
• Design and implement robust data generation pipelines for data generation.
• Design and evaluate experiments for model training.
• Develop robust data generation pipelines for model training.

Skills

Python

C++

Education

Master's degree or PhD in Computer Science or related

Tools

Jax

PyTorch

Job description

Waymo LLC in San Francisco is seeking a graduate student intern or entry-role software engineer focused on data and model development for the Waymo Driver platform. You will mine and curate driving data, and apply language models and foundation models for reasoning, perception, and decision making.

You will design data generation pipelines, run experiments for model training, and contribute to robust software engineering practices.