Senior RL Engineer for Autonomous Driving Systems

Aimlroles; Iowa (LA); On-site; USD 150,000 - 250,000; Full time

Benefits offered by this job

Health Care Plan (Medical, Dental &amp

Retirement Plan (Traditional and Roth

Life Insurance

Paid Time Off

Family Leave

Disability

Free Food & Snacks

Job summary

Pony.ai is seeking a senior ML engineer to build scalable systems for training and fine-tuning large generative models, driving evaluation coverage and scenario testing.

You will implement RL-style objectives, ship deep learning solutions, and own production-oriented ML for fleet-scale assessment while collaborating with Prediction, Planning, Research, and platform teams to deliver measurable improvements.

Qualifications

• MS/PhD in CS/ML or related field, or equivalent practical experience.
• Hands-on ML in production with a strong RL component.
• Deep learning, sequence modeling, and generative models.
• Strong publications or track record of shipping ML systems.

Responsibilities

• Build scalable systems for training and fine-tuning large generative models for evaluation and scenario coverage.
• Implement RL-style methods and reward/preference objectives for high-fidelity behaviors.
• Ship deep learning solutions (including LLM/VLM where appropriate) to automate workflows.
• Own production-oriented ML for fleet-scale assessment and monitoring.
• Design data and evaluation systems inspired by RLHF for scalable training loops.
• Partner with Prediction, Planning, Research, and platform/engineering teams to land cross-cutting improvements.

Skills

RL algorithms

Deep learning

Distributed training

Python

PyTorch

LLMs

Generative models

Publications

Leadership

Education

MS/PhD in CS/ML or related field

Tools

PyTorch

Job description

Pony.ai is seeking a senior ML engineer to build scalable systems for training and fine-tuning large generative models, driving evaluation coverage and scenario testing.

You will implement RL-style objectives, ship deep learning solutions, and own production-oriented ML for fleet-scale assessment while collaborating with Prediction, Planning, Research, and platform teams to deliver measurable improvements.