How to apply for Senior AI Controls Solutions Engineer (HVAC Systems)

About Phaidra

Phaidra builds AI-powered control systems for industrial facilities, using reinforcement learning to replace hard-coded rules. The team has applied AI at DeepMind (AlphaGo) and Google Data Centers (40% cooling energy reduction). This role is a chance to work on production AI for HVAC systems, not research prototypes.

About the role

As a Senior AI Controls Solutions Engineer, you will configure and deploy AI agents for HVAC systems in industrial settings. You will work with domain experts to define agent behavior without code, turning raw sensor data into control actions. Your work directly reduces energy waste and environmental impact.

A typical day

You might start by reviewing sensor data from a customer's HVAC system and adjusting AI agent parameters. Later, you could join a call with a domain expert to refine control objectives, then test changes in a simulation before deployment. You will document configurations and results to share with the team.

Who Phaidra is looking for

• Experience with HVAC systems, building automation, or industrial controls (e.g., BACnet, Modbus, PLCs).
• Strong understanding of reinforcement learning or other AI control methods, with production deployment experience.
• Ability to translate domain expert requirements into AI agent configurations without writing code.
• Comfortable working remotely and collaborating with cross-functional teams (engineers, domain experts, customers).

Tips for this application

• Highlight specific HVAC or industrial controls projects where you improved efficiency or reduced energy use.
• Show experience with reinforcement learning in production, not just theory. Mention any deployed AI control systems.
• Emphasize your ability to work with domain experts and configure AI without coding, as Phaidra's platform is no-code for users.
• Reference Phaidra's track record (AlphaGo, Google Data Centers) to show you understand their approach and impact.
• If you have experience with sensor data and KPIs in industrial settings, quantify results (e.g., % energy reduction).

What to cover in your cover letter

["Your hands-on experience with HVAC systems and how you've optimized them using data or AI.", 'A concrete example of deploying reinforcement learning or AI controls in a production environment.', "How you've collaborated with domain experts to define and implement control strategies without code.", "Why you want to work on industrial efficiency and reduce environmental impact, tying to Phaidra's mission."]

Research before applying

• Phaidra's technology: reinforcement learning for industrial control, no-code configuration for domain experts.
• Their case studies or blog posts on HVAC or building energy optimization, if available.
• The backgrounds of their team, especially those from DeepMind and Google Data Centers, to understand their AI approach.
• Common HVAC control challenges (e.g., setpoint optimization, chiller staging) and how AI can address them.

Likely interview topics

Based on the job description, expect questions about:

• How would you approach configuring an AI agent for an HVAC system with multiple zones and variable occupancy?
• Describe a time you deployed a reinforcement learning model in a real-world control system. What challenges did you face?
• What HVAC-specific sensors and KPIs would you prioritize for an AI control system, and why?
• How do you ensure safety and reliability when AI controls critical infrastructure like HVAC?
• Explain how you would work with a domain expert who is skeptical of AI to define agent objectives.

Common mistakes to avoid

• Focusing only on AI theory without showing practical HVAC or controls experience.
• Ignoring the no-code aspect: candidates who emphasize coding over configuring AI for domain experts.
• Not demonstrating understanding of industrial environments (e.g., safety, legacy systems, sensor limitations).