Data Center Reliability Engineer

Phaidra is building the future of industrial automation. As a Data Center Reliability Engineer, you will bridge the gap between infrastructure telemetry and actionable operational intelligence, utilizing data to diagnose and enhance data center operations.

Responsibilities:

• You will utilize our existing data ingestion and delivery platforms to "teach" our models to understand the physical world, filling a critical expertise gap in the data center industry
• Multidisciplinary Diagnostic Analysis: Use telemetry tools to analyze sensor data across mechanical (chillers, pumps) and electrical (UPS, switchgear, power feeds) systems to identify "failure signatures" for our LLM-driven monitoring tool
• Refining the Logic Engine: Act as a primary user of our platforms, identifying gaps in our current mechanisms and collaborating with Engineering to influence future features and data quality
• Operational Insight Generation: Translate raw telemetry into the "SME-level" logic and directions used by our LLM tool to guide data center operators in real-time
• SME Development: Cultivate deep domain expertise in all facets of data center infrastructure. You will be expected to master the nuances of both mechanical and electrical dependencies to ensure our product reflects operational reality
• Customer Guidance: Move from shadowing peers to directly supporting customers, using our platform to provide clear, data-backed direction on complex problems
• Model Validation: Oversee pilot projects to test how our AI-driven SME tool interprets real-world stressors, ensuring the output is operationally realistic, accurate, and actionable
• Adaptability: Remain agile and proactive. As a member of a fast-moving team, you will encounter challenges and scopes not explicitly defined here; we expect you to lean in and solve them

Requirements:

• 2–3 years of professional relevant experience
• Bachelor's degree in Mechanical Engineering, Electrical Engineering, Control Theory, or a related field that provides a foundation in physical systems and thermodynamics
• A deep, innate interest in using data to diagnose how and why systems fail
• Strong Python skills and experience with data manipulation libraries (Pandas/NumPy) to perform custom analysis outside of standard tooling
• Ability to explain complex diagnostic findings clearly and persuasively to both technical peers and non-domain stakeholders
• A proven ability to look at a problem without preconceived notions and figure out solutions either independently or via team collaboration
• Demonstrated commitment to Transparency, Collaboration, and Ownership—especially in environments where reliability and learning from failure are paramount
• Experience with critical infrastructure components (HVAC, power distribution, or industrial automation)
• Experience with time-series data from industrial sensors (SCADA, BMS, Smart Meters)
• Exposure to or a strong interest in how LLMs can be used for root-cause analysis and automated reporting