AI Research Scientist (ML for Physical Systems)

About Phaidra

Phaidra is building the future of industrial automation.

The world today is filled with static, monolithic infrastructure. Factories, power plants, buildings, etc. operate the same they've operated for decades — because the controls programming is hard-coded. Thousands of lines of rules and heuristics that define how the machines interact with each other. The result of all this hard-coding is that facilities are frozen in time, unable to adapt to their environment while their performance slowly degrades.

Phaidra creates AI-powered control systems for the industrial sector, enabling industrial facilities to automatically learn and improve over time. Specifically:

• 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 DeepMind's AlphaGo, 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: Transparency, Collaboration, Operational Excellence, Ownership, and Empathy. 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

Phaidra is based in the USA, but we are 100% remote with no physical office. We hire employees internationally with the help of our partner, OysterHR. Our team is currently located throughout the USA, Canada, UK, Italy, Sweden, Spain, Portugal, the Netherlands, Singapore, Australia, and India.

Who You Are

Research Scientists at Phaidra lead our efforts in developing novel algorithmic architectures with the goal of bringing intelligent control systems to the 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.

You bring a rare combination of deep expertise in machine learning and a strong understanding of physical systems—particularly in areas such as thermodynamics, fluid mechanics, and control systems. Your interdisciplinary background allows you to apply AI not only as a computational tool but as a principled framework to model, optimize, and control complex physical processes.

Drawing on expertise from a variety of disciplines including machine learning, reinforcement learning, optimization, and control theory, our Research Scientists are at the forefront of groundbreaking research and applying it to real-world industrial problems.

We are seeking a team member located within one of the following areas: UK (preferred), USA, or Canada.

• In the United States, we accept applicants located in the following states: California, Colorado, Connecticut, Georgia, Florida, Indiana, Maryland, Minnesota, Missouri, Nebraska, New York, North Carolina, Pennsylvania, South Carolina, Tennessee, Texas, Virginia, Washington. 
• In Canada, we accept applicants located in the following provinces: Ontario, British Columbia, and Alberta.

Responsibilities

• Collaborate with other AI researchers on applied real-world problems to demonstrate algorithmic feasibility and enhance algorithmic capabilities.
• Design and implement prediction and control algorithms for complex, nonlinear, and dynamic physical systems governed by principles of thermodynamics and fluid dynamics.
• Develop and maintain a benchmarking platform for algorithmic performance evaluation and experimental design.
• Clearly and efficiently report and present research findings and developments, both internally and externally, verbally and in writing.
• Participate in and organize ambitious collaborative research projects.
• Work with external collaborators and maintain relationships with relevant research labs and key individuals.