Senior AI Research Scientist; Model- RL

Senior AI Research Scientist (Model-based RL)
About Phaidra

Phaidra is building the future of industrial automation.

We use reinforcement learning algorithms to provide this intelligence, converting raw sensor data into high-value actions and decisions. We focus on industrial applications, which tend to be well-sensorized with measurable KPIs — perfect for reinforcement learning. We enable domain experts (our users) to configure the AI control systems (i.e. agents) without writing code. They define what they want their AI agents to do, and we do it for them.

Our team has a track record of applying AI to some of the toughest problems. From achieving superhuman performance with Deep Mind’s Alpha Go, to reducing the energy required to cool Google’s Data Centers by 40%, we deeply understand AI and how to apply it in production for massive impact.

Phaidra’s ability to achieve its mission is determined by our ability to work together — as defined by our core values:
Agency, Velocity, Craft, and Truth. We seek individuals who embody these values, as they are instrumental in ensuring our team consistently delivers excellence and fosters an engaging and supportive culture.

Research Scientists at Phaidra lead our efforts in developing novel algorithmic architecture towards the end goal of bringing intelligent control systems to industrial sector. Having pioneered research in the world’s leading academic and industrial labs as PhDs, post-docs or professors, Research Scientists join Phaidra to work collaboratively within and across research fields. Drawing on expertise from a variety of disciplines including model-based reinforcement learning, planning and optimal control, deep learning, world models, and safe reinforcement learning, our Research Scientists are at the forefront of groundbreaking research and applying it to real-world industrial control systems.

• Design, implement, and evaluate model-based reinforcement learning agents — including planning-based controllers (MPC, MPPI) — and the software prototypes needed to deploy them on real industrial control systems.
• Develop learned dynamics and world models (learned surrogates) that generalize across systems, including the training pipelines — pretraining, curriculum learning, active/adversarial learning, and fine-tuning — needed to make them reliable for planning and control.
• Research and implement methods for e.g. safe RL, constrained control, scenario planning and Bayesian RL, to develop agents that satisfy safety constraints during deployment.
• Report and present research findings and developments including status and results clearly and efficiently both internally and externally, verbally and in writing.
• Participate in and organize ambitious collaborative research projects, and work with external collaborators and partners to translate research into production outcomes.
• Mentor and guide Research Engineers to apply research findings and developments to industrial domains.
• Independently defines new research directions
• Translates research into practical outcomes
• Owns the development and rollout for an entire research area or large project

• PhD in a technical field or equivalent practical experience, with a strong background in model-based reinforcement learning and demonstrated knowledge in one or more of the following:
• Planning algorithms
• World models / learned dynamics surrogates
• Reinforcement Learning and Deep Learning
• Control Theory
• Safe / constrained RL

Either:

• 2+ years of research experience in academia or industry after PhD graduation.
• 5+ years of research experience in academia or industry after Master’s graduation.

• Extensive research in the fields of {Model Based, Model Free, Safe} RL and Control Theory, with…