Senior Staff Research Engineer – Reinforcement Learning for AI Agents at XPENG

Santa Clara, California, United States -

Full Time

Start Date

Immediate

Expiry Date

17 Jun, 26

Salary

413160.0

Posted On

19 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Reinforcement Learning, Large Language Models, AI Agents, Policy Optimization, RLHF, RLAIF, Agent Training Pipelines, Benchmarking Systems, PPO, Actor-Critic, Policy Gradient Methods, Python, PyTorch, JAX, Preference Learning, Multi-Agent Systems

Industry

Motor Vehicle Manufacturing

Description

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. 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). Agent training pipelines built on top of our agent infrastructure platform. Evaluation and benchmarking systems for agent capabilities. Learning loops that integrate real-world and simulation data. Contribute to AI systems that continuously improve after deployment. Basic Qualifications 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. Preferred Qualifications 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.

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Responsibilities

The role involves designing learning systems for AI agents to plan over long horizons, learn effective strategies, and improve through experience, focusing on reinforcement learning methods for LLM-driven agents and decision systems. Key tasks include policy optimization, learning from feedback (RLHF/RLAIF), building agent training pipelines, and developing evaluation systems that integrate real-world and simulation data.