pony.ai

Full-stack autonomous driving platform licensing

Machine Learning Engineer - Reinforcement Learning

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeFremont, CA, USAIn-person

About the Job

Requirements

A Master of Science or Ph.D. in Computer Science, Machine Learning, AI, or a related field, or equivalent practical experience.
Hands-on experience building and applying machine learning in production-grade settings, with a strong reinforcement-learning component such as policy learning, preference/feedback optimization, or offline/online reinforcement-learning pipelines.
Depth in deep learning, sequence modeling, and generative models.
Demonstrated impact through strong publications or a clear history of shipping impactful machine-learning 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.

Responsibilities

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 reinforcement-learning-style methods, including algorithms, reward/preference objectives, and training setups suited to high-fidelity, insightful behaviors in simulation-aligned workflows with a closed-loop evaluation mindset.
Ship deep-learning solutions, including large language models and vision-language models 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 machine learning for fleet-scale assessment, including training, optimization, monitoring, and iteration of models used to judge performance across large real-world exposure.
Design and evolve data and evaluation systems inspired by reinforcement learning from human preferences and related paradigms, turning preference/judgment signals into repeatable, scalable training and evaluation loops.
Partner with Prediction, Planning, Research, and platform/engineering leads to deliver cross-cutting improvements with clear metrics.

Desired Qualifications

Background in autonomous vehicles, robotics, or complex simulation environments.
Strong grasp of modern reinforcement learning and post-training techniques in large language models, diffusion language models, vision-language-action models, and video generation.
Hands-on integration of simulation platforms with machine-learning training and evaluation workflows.
Fluency in Python and experience with frameworks such as PyTorch.
Experience defining and operating metrics for complex, safety-critical artificial intelligence systems.
Technical leadership, including influencing stakeholders, aligning teams, and raising the bar for evaluation rigor.

Machine Learning Engineer - Reinforcement Learning

pony.ai · Fremont, United States · Full-time · Posted 2026-10-26

Workplace: on_site

Description

Founded in 2016 in Silicon Valley, Pony.ai has quickly become a global leader in autonomous mobility and is a pioneer in extending autonomous mobility technologies and services at a rapidly expanding footprint of sites around the world. Operating Robotaxi, Robotruck and Personally Owned Vehicles (POV) business units, Pony.ai is an industry leader in the commercialization of autonomous driving and is committed to developing the safest autonomous driving capabilities on a global scale. Pony.ai’s leading position has been recognized, with CNBC ranking Pony.ai #10 on its CNBC Disruptor list of the 50 most innovative and disruptive tech companies of 2022. In June 2023, Pony.ai was recognized on the XPRIZE and Bessemer Venture Partners inaugural “XB100” 2023 list of the world’s top 100 private deep tech companies, ranking #12 globally. As of August 2023, Pony.ai has accumulated nearly 21 million miles of autonomous driving globally. Pony.ai went public at NASDAQ in November 2024.

Responsibility

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.

Requirements

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.

Compensation and Benefits

Base Salary Range: $150,000 - $250,000 Annually

Compensation may vary outside of this range depending on many factors, including the candidate’s qualifications, skills, competencies, experience, and location. Base pay is one part of the Total Compensation and this role may be eligible for bonuses/incentives and restricted stock units.

Also, we provide the following benefits to the eligible employees:

Health Care Plan (Medical, Dental & Vision)
Retirement Plan (Traditional and Roth 401k)
Life Insurance (Basic, Voluntary & AD&D)
Paid Time Off (Vacation & Public Holidays)
Family Leave (Maternity, Paternity)
Short Term & Long Term Disability
Free Food & Snacks

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About the Company

pony.ai

Full-stack autonomous driving platform licensing

Pony.ai develops Level 4 autonomous driving technology with a full-stack, vehicle-agnostic Virtual Driver that combines software and hardware. Its main offerings are Robotaxi, Robotruck, and POV licensing, with the PonyPilot app enabling fare-based robotaxi services in Beijing, Guangzhou, Shanghai, and Shenzhen, and autonomous freight solutions like truck platooning. The company differentiates itself through automotive-grade hardware, cost-focused design, and strategic partnerships with Toyota, Sany, and Dongfeng Liuzhou Motor, plus a global footprint in Silicon Valley and China. Its goal is to scale Level 4 autonomous mobility by expanding licensing, deploying robotaxi and robotruck services, and broadening automotive partnerships to build a wide, deployed AV ecosystem.

Company size: 501-1,000
Company stage: IPO
Headquarters: Fremont, CA
Founded: 2016

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