强化学习工程师
Reinforcement Learning Engineer
强化学习工程师 - 远程办公
Bright Vision Technologies 是一家技术咨询和软件开发公司,为美国各地的客户提供云计算、人工智能、数据和企业解决方案。
这是一个加入一家知名且受人尊敬的组织的绝佳机会,提供巨大的职业发展潜力。
职位名称:强化学习工程师
工作地点:100% 远程(美国)
职位类型:全职,直接W2
薪资范围:每年10万至15万美元
所需经验:6年以上
赞助:美国公民、绿卡持有者、EAD持有者以及H-1B转签候选人欢迎申请。我们无法为该职位的新H-1B签证申请提供赞助。
主要职责
· 为真实和模拟环境中的序列决策问题设计和实现强化学习解决方案。
- 开发、校准和维护适合大规模智能体训练的仿真环境。
- 实现并评估现代强化学习算法,包括策略梯度、演员-评论家、离策略和离线强化学习方法。
- 设计奖励函数和塑造策略,使智能体行为与期望结果和安全约束保持一致。
- 在探索成本高或不安全的情况下应用离线强化学习和模仿学习技术。
- 在相关情况下使用RLHF、DPO和相关技术对大型语言模型进行微调。
- 构建可扩展的分布式强化学习训练基础设施,包括高效的体验收集和回放系统。
- 通过算法和工程改进优化训练稳定性和样本效率。
- 设计严格的评估协议,包括分布外和对抗性测试案例。
- 实现安全机制,如约束执行、保守策略和人工在环监督。
- 与应用科学家和产品团队合作,识别高价值的强化学习应用场景。
- 监控生产环境中部署的策略和模型,检测漂移、退化和意外行为,构建警报和仪表板,在问题显著影响用户之前发现它们。
- 为内部利益相关者记录方法、设计决策和操作特性。
- 跟踪强化学习研究,并将有前景的技术转化为生产就绪的解决方案。
所需资格
· 计算机科学、机器学习或相关领域的硕士或博士学位;或同等的实际工作经验。
- 六年或以上相关工作经验
查看英文原文
Reinforcement Learning Engineer - Remote
Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.
This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.
Job Title: Reinforcement Learning Engineer
Location: 100% Remote (U.S.)
Position Type: Full-time, Direct W2
Salary Range: $100,000–$150,000 Annually
Experience Required: 6+ years
Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.
Key Responsibilities
· Design and implement reinforcement learning solutions for sequential decision-making problems in real and simulated environments.
- Develop, calibrate, and maintain simulation environments suitable for large-scale agent training.
- Implement and evaluate modern RL algorithms including policy gradient, actor-critic, off-policy, and offline RL methods.
- Engineer reward functions and shaping strategies that align agent behavior with desired outcomes and safety constraints.
- Apply offline RL and imitation learning techniques where exploration is costly or unsafe.
- Use RLHF, DPO, and related techniques for fine-tuning large language models when relevant.
- Build scalable training infrastructure for distributed RL, including efficient experience collection and replay systems.
- Optimize training stability and sample efficiency through algorithmic and engineering improvements.
- Design rigorous evaluation protocols, including out-of-distribution and adversarial test cases.
- Implement safety mechanisms such as constraint enforcement, conservative policies, and human-in-the-loop oversight.
- Collaborate with applied scientists and product teams to identify high-value RL use cases.
- Monitor deployed policies and models in production for drift, regression, and unintended behaviors, building the alerting and dashboards that surface issues before they meaningfully affect users.
- Document methodology, design decisions, and operational characteristics for internal stakeholders.
- Stay current with RL research and translate promising techniques into production-ready solutions.
Required Qualifications
· Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent applied experience.
- Six or more years of combined RL research and engineering experience.
- Strong proficiency in Python and modern deep learning frameworks.
- Hands-on experience with at least one major RL library or in-house RL stack.
- Solid understanding of probability, optimization, and the theoretical foundations of RL.
- Experience designing and tuning reward functions in non-trivial environments.
- Familiarity with simulation environments and large-scale experience collection.
- Experience training neural network policies on GPU clusters.
- Strong written and verbal communication skills.
- Track record of shipping or publishing impactful RL work.
Preferred Qualifications
· Experience with RLHF for large language models.
- Familiarity with multi-agent RL or hierarchical RL.
- Exposure to robotics, control systems, or autonomous driving.
- Publications in RL or related research venues.
- Open-source contributions to RL libraries or environments.
How to Apply
Would you like to know more about this opportunity? For immediate consideration, please send your resume to or contact us at (908) 505-3899. Learn more about Bright Vision Technologies at .
Bright Vision Technologies is an Equal Opportunity Employer.
Equal Employment Opportunity (EEO) Statement
Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.
BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.
Originally posted on Himalayas