Hybrid Senior VP, Reinforcement Learning

at Resaro

• Job title: Senior VP, Reinforcement Learning
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Postgraduate Degree
• Tech skills: IoT
• Location requirements: Hybrid, Munich, Germany

About the role

• Lead architect for TEVV product suite at Resaro, focusing on reinforcement learning systems for AI testing and assurance. Collaborating with a global team to ensure safety and robustness in real-world applications.

Responsibilities

• Independently implement Resaro’s RL validation prototype to expose agent instability and vulnerability in a mission-critical and complex environment.
• Scale, lead and mentor a global, cross-functional, high-performing team of AI researchers and engineers, drawing on experience steering organizations of 30+ experts.
• Define the long-term vision and technical roadmap for RL TEVV, focusing on validating RL algorithms and learned policies in complex environments with mission-critical applications across system control, autonomous vehicles, and robotics.
• Advance methods for learning probabilistic reward functions from human feedback (RLHF) to align AI behavior with mission goals.
• Partner with Product Management to translate product vision, customer problems, and market opportunities into end‑to‑end solution architecture and technical roadmaps that support a product-led growth strategy.

Requirements

• Master / Ph.D. in Robot Reinforcement Learning or a closely related field.
• Proven track record in developing and implementing novel RL and ML algorithms, e.g. research or commercial implementation.
• Demonstrated deep theoretical understanding of and practical experience with the RL framework, including bandit setting, (in-)finite horizon setting, on- and off-policy RL, and trust-region RL approaches.
• Experience in Bayesian Machine Learning and probabilistic models.
• Understanding of AI/ML/RL lifecycle and the state-of-the-art approaches and limitations of testing and validating complex use cases.
• Strong skills in requirements gathering, stakeholder communication, and solution scoping.
• Experience with fully differentiable deep learning for highly unstable systems (nice-to-have).
• Experience with Active Learning and RLHF (nice-to-have).
• Background in model compression and pruning for deploying large RL models onto edge devices (nice-to-have).
• Hands-on experience with Bayesian Meta-Learning to reduce training time and absolute error in complex models (nice-to-have).
• A strong portfolio of innovation, including multiple successful paper submissions at conferences like NeurIPS, ICML, ICLR, IROS, ICRA, CoRL, and a deep patent history (e.g., 17+ patents) (nice-to-have).
• Experience spearheading global AI initiatives and delivering AI solutions for both B2G (Unmanned Systems) and B2B (IoT) sectors (nice-to-have).
• Demonstrated success in leading cross-functional teams to deliver technical solutions (nice-to-have).
• Knowledge of deployment constraints in high-security or classified environments (nice-to-have).
• Prior exposure or experience with directly engaging senior stakeholders from Director to C-suite level (nice-to-have).
• Prior security clearance at Government CONFIDENTIAL and above (nice-to-have).

Benefits

• Health insurance
• Flexible working hours
• Professional development opportunities