Senior / Staff Machine Learning Engineer, Vehicle Dynamics
你将...设计和开发基于数据的，机器学习方法来解决车辆控制问题，使现代 ML 成为传统方法解决的领域。开发学到的车辆行为和动态模型，并将它们集成在闭环模拟中。应用机器学习以改善控制器如何适应车辆和操作条件。加入一个跨学科工程师和研究科学家团队，使用人工智能先决的方式实现安全自驾规模。自有问题的终结，从概念化和离线实验到模拟和车载验证。建立数据管道，评估指标和工具来测量学习方法是否超越了经典基线。参与并分享技术和架构讨论中的想法，帮助定义安全关键堆中学习和古典控制如何共存。资格：MS/博士或学士学位，至少有4年的行业经验在机器人，控制，机械/电气工程，计算机科学及/或类似的技术领域) 研究。证明了控制理论和动态系统 （例如 MPC,最佳控制，状态估算，系统识别，动力和动态车辆建模）. 实践经验将机器学习应用到物理系统中，使用实际硬件而不是单独模拟。在 Python 和 C++中生产质量编码技能以及 Pytorch 等深度学习框架的经验。使用线性代数，优化，统计和概率解决问题的坚实的技巧。快速原型设计和测试新算法的能力，并设计证明它们是否有效的实验能力。开放的心态和协作团队玩家愿意帮助他人。对自动驾驶技术，解决困难问题和创新的解决方案有热情。该角色的美国年薪范围为：根据公司的薪酬实践，WABI US Inc. 年度工资范围是基于几个因素决定的。注：该公司为本职位员工提供额外的薪酬，包括股权激励奖项和年度绩效奖金。
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You Will…
Design and develop data-driven and machine-learned approaches to vehicle control problems, bringing modern ML to a domain traditionally solved with classical methods.
Develop learned models of vehicle behavior and dynamics, and integrate them into the closed-loop simulation.
Apply machine learning to improve how the controller adapts across vehicles and operating conditions.
Be part of a team of multidisciplinary Engineers and Research Scientists using an AI-first approach to enable safe self-driving at scale.
Own problems end to end, from conceptualization and offline experimentation through simulation and on-vehicle validation.
Build the data pipelines, evaluation metrics, and tooling needed to measure whether a learned approach outperforms the classical baseline.
Participate and share ideas in technical and architecture discussions, helping define how learning and classical control coexist in a safety-critical stack.
Qualifications:
MS/PhD or Bachelors degree with a minimum of 4 years of industry experience in Robotics, Controls, Mechanical/Electrical Engineering, Computer Science and/or similar technical field(s) of study.
Demonstrated depth in control theory and dynamic systems (e.g., MPC, optimal control, state estimation, system identification, kinematic and dynamic vehicle modeling).
Hands-on experience applying machine learning to a physical system, with real hardware in the loop rather than simulation alone.
Production-quality coding skill in Python and C++, and experience with deep learning frameworks such as PyTorch.
Solid problem solving skills using linear algebra, optimization, statistics & probability.
Ability to rapidly prototype and test new algorithms, and to design the experiments that prove whether they work.
Open-minded and collaborative team player with the willingness to help others.
Passionate about self-driving technologies, solving hard problems, and creating innovative solutions.
The US yearly salary range for this role is: $241,000 - $320,000 USD in addition to competitive perks & benefits. Waabi US Inc.’s yearly salary ranges are determined based on several factors in accordance with the Company’s compensation practices. Note: The Company provides additional compensation for employees in this role, including equity incentive awards and an annual performance bonus.