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# Machine Learning Engineer - Reinforcement Learning

- **Company:** Pony.AI, Inc.
- **Location:** Fremont, United States
- **Salary:** $150,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Distributed Computing Environment, Python (Programming Language), Machine Learning, Pytorch, Large Language Models, Deep Learning, Information Technology, Machine Learning Operations
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/machine-learning-engineer-reinforcement-learning-ponyai-8000933

## About the Role

* M.S. or Ph.D. in Computer Science, Machine Learning, AI, or a related field-or equivalent practical experience.
 * Hands-on experience building and applying ML in production-grade settings, with a strong RL component (policy learning, preference/feedback optimization, or offline/online RL pipelines).
 * Depth in deep learning, sequence modeling, and generative models.
 * Demonstrated impact via strong publications or a clear history of shipping impactful ML systems end-to-end.
 * Experience with large-scale distributed training and large-scale data processing.
 * Ability to lead ambiguous technical work from problem framing through reliable delivery.

Preferred

 * Background in autonomous vehicles, robotics, or complex simulation environments.
 * Strong grasp of modern RL and post-training techniques in LLM, dLLM, VLA and video generations.
 * Hands-on integration of simulation platforms with ML training and evaluation workflows.
 * Python fluency and frameworks such as PyTorch
 * Experience defining and operating metrics for complex, safety-critical AI systems.
 * Technical leadership: influencing stakeholders, aligning teams, and raising the bar for evaluation rigor.
 * Excellent communication-simple explanations of complex trade-offs.

## Description

* Build scalable systems for training and fine-tuning large generative models that produce realistic, informative driving behaviors for evaluation and scenario coverage.
 * Implement and iterate on RL-style methods: algorithms, reward / preference objectives, and training setups suited to high-fidelity, insightful behaviors in simulation-aligned workflows (closed-loop evaluation mindset).
 * Ship deep learning solutions (including LLM / VLM where appropriate) that improve human-led triaging, automate high-volume workflows, and support nuanced analysis of self-driving behavior to surface critical anomalies.
 * Own production-oriented ML for fleet-scale assessment: training, optimization, monitoring, and iteration of models used to judge performance across large real-world exposure.
 * Design and evolve data + evaluation systems inspired by RL from human preferences (RLHF) and related paradigms-turning preference/judgment signals into repeatable, scalable training and evaluation loops.
 * Partner broadly with teams such as Prediction, Planning, Research, and platform/engineering leads to land cross-cutting improvements with clear metrics.


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