Senior Staff Research Engineer – Reinforcement Learning for AI Agents

United States·Santa Clarasenior

Data ScienceOtherDevOps & InfrastructureResearch EngineerAi Research EngineerData & AI

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Quick Summary

Overview

XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics.

Key Responsibilities

Reinforcement learning methods for LLM-driven agents and decision systems. Policy optimization for long-horizon reasoning and planning. Learning from human or AI feedback (RLHF / RLAIF).

Requirements Summary

Experience with RLHF or preference learning. Experience with LLM agents or tool-using AI systems. Multi-agent systems or long-horizon planning. Simulation environments for RL. Publications in NeurIPS, ICML, ICLR, ACL , or related venues.

Technical Tools

pythonpytorchmachine-learning

XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity.

We are looking for exceptional Research Engineers / Scientists to design learning systems that allow agents to plan over long horizons, learn effective strategies, and improve through experience.

This role sits at the intersection of reinforcement learning, large language models, and real-world autonomous systems. Autonomous systems must operate reliably in complex, dynamic environments. We believe the next generation of autonomy will involve learning agents that continuously improve through interaction, feedback, and large-scale data. You will help build the learning systems that power these agents.

Responsibilities

~1 min read

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Reinforcement learning methods for LLM-driven agents and decision systems.
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Policy optimization for long-horizon reasoning and planning.
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Learning from human or AI feedback (RLHF / RLAIF).
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Agent training pipelines built on top of our agent infrastructure platform.
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Evaluation and benchmarking systems for agent capabilities.
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Learning loops that integrate real-world and simulation data.
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Contribute to AI systems that continuously improve after deployment.

Requirements

~1 min read

MS or PhD in Computer Science, AI, Machine Learning, Robotics, or a related field.
Strong background in reinforcement learning or machine learning.
Experience implementing RL algorithms such as PPO, Actor-Critic, or policy gradient methods.
Strong programming skills in Python with PyTorch or JAX.
Experience building ML training systems or infrastructure.

Requirements

~2 min read

Experience with RLHF or preference learning.
Experience with LLM agents or tool-using AI systems.
Multi-agent systems or long-horizon planning.
Simulation environments for RL.
Publications in NeurIPS, ICML, ICLR, ACL, or related venues.

What do we provide:

A fun, supportive and engaging environment.
Opportunity to make significant impact on transportation revolution by the means of advancing autonomous driving.
Opportunity to work on cutting edge technologies with the top talent in the field.
Competitive compensation package.
Snacks, lunches and fun activities.

The base salary range for this full-time position is $244,140 - $413,160, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

We are an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other prescribed category set forth in federal or state regulations.