Senior Diagnostics Engineer (Data Center Operations)

Phaidra builds AI-powered control systems for industrial facilities. The Senior Diagnostics Engineer owns diagnostics across customer deployments, analyzing operational data to identify root causes, validate AI tools, and improve system performance. The role also encodes operational expertise into the platform and supports customer-facing teams with data-backed guidance.

Responsibilities:

• Own a portfolio: Serve as the Data Science owner across a large set of concurrent customer deployments. Proactively hunt for problems in your portfolio without waiting to be pulled in, and know which of your systems are drifting before anyone tells you
• Break our product on purpose: Use Phaidra's platform daily to troubleshoot real customer systems. When the tool cannot answer your question, characterize the gap precisely and drive the fix into the product
• Diagnose across disciplines: Analyze mechanical and electrical telemetry as well as CMMS and alarm data to find failure signatures, performance degradation, and control pathologies, and trace symptoms to true root cause
• Encode your expertise: Convert telemetry and operational judgment into the SME-level logic our AI tooling uses to direct operators in real time
• Validate the models: Run pilots that stress our AI-driven tools against real conditions, and hold the line on whether the output is operationally realistic
• Support the front line: Partner with Solutions Engineering and TPMs, who own the customer relationship, and step into direct customer conversations when they need your depth. Give data-backed direction that actually gets implemented
• Raise the team: Mentor your peers across all teams. Actively collaborate with others and share learnings in a way that makes everyone better within the company
• Lean in: You will hit challenges and scopes not written here. Solve them

Requirements:

• 8-12 years of experience in facilities, plant, industrial, or manufacturing operations, engineering, or controls, with a track record of using data analysis to materially improve how those systems perform
• Deep cooling systems expertise: Chiller plant fundamentals, configurations, and sequences of operation. Applied thermodynamics, hydronics, refrigeration cycles, and airside behavior
• Analytical horsepower: Strong Python with Pandas and NumPy, and the ability to do custom analysis on industrial time-series data when standard tooling falls short
• Portfolio-scale ownership: Proven as a senior individual contributor across many concurrent sites or projects, with sound judgment about where to spend your attention
• Relentless curiosity: A deep interest in how and why systems fail. You would rather solve a live problem than write a paper about it
• Unbiased problem-solving: You walk into a problem without preconceived notions and work it out, independently or with the team
• Communication mastery: You can explain a complex diagnostic finding to a plant operator, a VP, and a software engineer, and have all three walk away with what they need
• Collaborative by default: You treat peers as fellow experts and measure success by the team's outcomes, not just your own portfolio
• AI-native working style: You will use LLMs daily here. No AI/ML background required, but we expect you to use these tools strategically and help others do the same
• Equivalent depth earned through operational experience is welcome
• Shares our values: Agency, Velocity, Craft, Truth
• We are seeking teammates who are based in the United States or Canada.
• Phaidra only hires individuals who are legally authorized to work in the specified location(s) above
• Candidates requiring visa sponsorship, either now or in the future, are not eligible for hire
• Mechanical Engineering, Electrical Engineering, Control Theory, Chemical Engineering, or a related field grounded in physical systems and thermodynamics is preferred
• Time-series data from industrial sensors: SCADA, BMS, EMS, historians, smart meters
• Working knowledge of industrial and commercial control protocols such as BACnet and Modbus
• Data Centers, district energy, thermal storage, industrial refrigeration, or other large-scale thermal systems
• A customer-facing or multi-site role where you owned outcomes you did not fully control
• Optimization problems in a thermal or controls context
• Mentoring analysts or engineers newer to data work than you