AI Research Scientist

Phaidra has launched Phaidra Prism, an AI platform designed to optimise data centre operations for AI workloads, at NVIDIA GTC in San Jose. The platform provides detailed visibility and collaborative intelligence to help operators maximise tokens per watt whilst limiting operational expenses. Phaidra Prism functions as an AI assistant built specifically for data centre operators and technicians. Users interact with equipment through a customised large language model to enhance observability, conduct root cause analysis and issue automated incident responses before issues affect service level agreements. Founded in 2019 by former Google, DeepMind and Trane engineers, Phaidra is an NVIDIA DSX Partner. CEO Jim Gao noted that traditional data centre operation methods no longer suffice as facilities scale to gigawatt-size with increasingly complex infrastructure and higher downtime costs.

Phaidra, CoreWeave and Applied Digital pioneer NVIDIA Max-Q AI factories with agentic liquid cooling management. March 16, 2026 - Phaidra today announced a groundbreaking methodology to drastically improve the thermal stability of liquid-cooled AI data centers. This methodology is outlined in the joint white paper "AI Agents for Liquid-Cooled AI Factories." By successfully leveraging AI-driven, feed-forward control systems on production NVIDIA Grace Blackwell platforms, the collaboration is paving the way for the future of "DSX AI factories" - a new operational paradigm where power, cooling, and workload management are unified to maximize efficiency and computational throughput. Phaidra has integrated NVIDIA DSX Max-Q to run GPU clusters as efficiently as possible, so more of the available power can go towards running AI workloads. The challenge of AI thermal volatility. Modern AI factories are fundamentally different from traditional data centers: defined by massive scale, extreme density, and highly synchronized workloads. Operators of large-scale AI factory campuses, such as Applied Digital, must manage increasingly complex interactions between power infrastructure, liquid cooling systems and rapidly fluctuating GPU workloads for their partners. When massive AI training or inference jobs are dispatched, thousands of networked GPUs ramp up simultaneously, creating "peaky" power profiles that can jump from idle to maximum capacity within seconds. Traditional liquid cooling relies on Proportional-Integral-Derivative (PID) controllers, which wait for a sensor to register a coolant temperature change before taking action. Because coolant has high thermal inertia, this reactive feedback loop suffers from a 3-to-5-minute delay, resulting in rapid heat spikes that force GPUs to throttle performance to protect themselves. To mitigate this, operators significantly over-cool their facilities to create a safety buffer - a strategy that wastes massive amounts of energy and limits overall compute capacity. The AI-driven solution. To close this latency gap, Phaidra developed a self-learning reinforcement learning (RL) AI Agent that fundamentally changes how cooling is managed. Instead of reacting after-the-fact to temperature changes, the AI Agent uses real-time rack power data as a leading indicator to predict and prevent thermal spikes. The agent seamlessly sends optimal setpoint commands to the Coolant Distribution Unit (CDU) before the heat fully registers in the fluid, reducing the effective response delay from minutes to under 10 seconds in validated production environments. Proven results at gigawatt scale. The new methodology underwent rigorous joint A/B testing in live production environments, including an NVIDIA DGX SuperPOD cluster running LLM training workloads and CoreWeave's NVIDIA GB200 NVL72 environments. The results were transformative: * Massive reduction in thermal overshoot: The AI Agent successfully reduced the magnitude of thermal spike overshoots by 75% to 80% compared to optimally-tuned PID baselines during sudden load ramps. * Unprecedented scale: Following this successful validation, Phaidra and CoreWeave are scaling the deployment of these AI agents throughout CoreWeave's liquid-cooled fleet, bringing AI-driven thermal management to its next generation of data center capacity. The pathway to Max-Q AI factories. Phaidra has integrated NVIDIA DSX Max-Q to operate the entire AI factory as a single unit of compute, at scale. By deep integration of Information Technology (IT) and Operational Technology (OT), this collaboration bridges the divide between white space compute and facility operations. With thermal stability secured by Phaidra's AI agents, facilities can safely raise their supply water temperatures, significantly reducing the burden on facility chillers. This provides the foundation for the next phase of the collaboration, where operators dynamically shift stranded power from the cooling system to revenue-generating IT compute. For a baseline 1GW AI factory, raising the coolant temperature safely could unlock billions in additional annual revenue. "In a world where computational resources are limited by energy availability, every watt that isn't being used for valuable token generation is a wasted watt," the joint white paper states. By co-designing power, cooling, and workload management systems, Phaidra, CoreWeave, NVIDIA, and critical infrastructure partner Applied Digital, are setting a new standard for reliability, end-user SLAs, and peak operational efficiency in the age of AI. AI agents for liquid-cooled AI factories Read Phaidra's white paper to learn how to prevent GPU throttling and safely increase facility temperatures to drive more revenue-generating compute. Featured Expert Co-Founder, Chief Executive Officer Jim is a co-founder and the CEO of Phaidra. He sets the strategic direction and leads the company in operational excellence. Prior to Phaidra, Jim led the DeepMind Energy Team and pioneered Google's use of AI controls on their hyperscale data center cooling systems. Prior to DeepMind, he spent a decade working as a Technical Lead for Google's Data Centers. Product | March 03, 2026 Phaidra Prism is an AI assistant designed by data center experts for data center operators and technicians. AI | January 06, 2026 An AI factory operates more like a formula 1 racecar, not a typical data center. Find out how Phaidra's AI agents deliver real-time thermal control for synchronized GPU workloads at gigawatt scale while working with a broader partner ecosystem. Research | December 02, 2025 What kind of data is needed to safely operationalize predictive AI control? Building on a previous blog, class 3 and 4 data provide the context and simulated future states that power real-world autonomy in industrial AI control systems.

Creating AI agents for gigawatt-scale AI factories with NVIDIA Omniverse DSX Blueprint. This was a big week for Phaidra and the broader AI factory ecosystem. During his keynote address at GTC DC, NVIDIA founder and CEO Jensen Huang unveiled the NVIDIA Omniverse DSX Blueprint - a comprehensive and open blueprint for designing and operating gigawatt-scale AI factories. Phaidra is proud to build the future of AI factories (i.e. data centers designed specifically for massive AI workloads) alongside NVIDIA and the other DSX ecosystem members. Phaidra, Inc. envisage a future where AI factories operate continuously at peak performance - aided by AI agents that vigilantly manage the complex infrastructure on a 24/7 basis. Jensen announcing the NVIDIA Omniverse DSX Blueprint with Phaidra and others at GTC DC Why now? Two major forcing functions are driving the need for the Omniverse DSX Blueprint: * Inefficiencies get magnified at gigawatt-scale: a single GW AI factory represents both a $50Bn investment and $200Bn revenue opportunity. Every 1% of inefficiency therefore represents $2Bn in lost revenue. * Extreme performance requires extreme co-design: AI factories today are so large, complex, and interconnected that they must operate as a single integrated machine rather than a collection of loosely-orchestrated components. This is the pathway towards step function tokens/watt improvements. By openly sharing a reference architecture for the entire industry to build upon, Phaidra, Inc. make it easier for everyone to collaboratively and iteratively improve energy efficiency, time-to-value, and reliability at scale. Phaidra's unique contributions to the Omniverse DSX Blueprint are: * Helping define the open data exchange standards and communications protocols that enable the various data center control planes (i.e. BMS, PMS, workload manager, etc.) to freely and openly communicate with each other. * Ensuring readiness for the new generation of agentic AI companies like Phaidra, Emerald AI, and others to readily integrate with the physical and digital twin parts of the AI factory - in pursuit of extreme performance. * Developing and sharing OpenUSD SimReady assets, and utilizing existing SimReady assets within the NVIDIA Omniverse digital twin, to rapidly train and evaluate its AI agents. This makes it easier for everyone to leverage robust simulation capabilities to improve AI factory performance. The benefits of a collaborative, simulation-first approach are clear. Within the GTC DC keynote demo, an animation of Phaidra's liquid cooling AI agent was shown in action (reposted below). Phaidra's self-learning AI agent is substantially better at reducing thermal spikes than traditional control systems. This AI agent eliminates the thermal spikes resulting from massively synchronized AI workloads, which in turn enables the AI factory to run at higher TCS temperatures. The end result is substantially less power and capital required for cooling, and more power available for revenue-generating GPUs. Its AI agents were developed and trained in simulation before they were deployed into real-world production systems. Phaidra collaborated closely with NVIDIA's data center engineering team and used an operational digital twin of an NVIDIA DGX SuperPOD to rapidly prototype various AI agent architectures under varying environmental conditions. The optimal AI agent was trained further in simulation (i.e. bootstrapped on synthetic data) before testing on production NVIDIA DGX GB200 systems with live AI workloads. Interestingly, Phaidra, Inc. discovered that the AI agent trained in simulation dramatically improved the performance of the existing liquid cooling control system that had already been fine-tuned by human experts. This is shown in the graph below, which illustrates the before/after performance of its AI agents at providing precision thermal control in liquid cooling systems. The red dots correspond to the traditional control system's performance given a 30-70% load ramp. The blue dots correspond to an AI agent that had been trained in simulation. Finally, the green dots correspond to an AI agent after several hours of live learning on the production system (i.e. the reinforcement learning-based AI agent taught itself to get better without human intervention). Phaidra's AI agent teaching itself to get better at managing AI factory infrastructure without human intervention. This mini-case study illustrates two important points: * Design and iteration in simulation can greatly accelerate your product development. What would have taken multiple quarters of iteration time (because this is mission-critical infrastructure) instead took weeks in simulation. * AI agents trained in simulation can already outperform existing systems - provided the simulation environment is of sufficiently high fidelity. The Phaidra team is proud to work alongside NVIDIA and the team of industry-leaders to define the future of gigawatt-scale AI factories. Its goal is to openly share its learnings and know-how through the Omniverse DSX Blueprint initiative to ensure that AI factories can readily leverage agentic AI technologies to achieve extreme energy efficiency, time-to-value, and reliability.