How to apply for Solutions Engagement Manager (AI/ML)

About AZX, PBC.

AZX works on AI transformation for climate and sustainability problems. It builds physics-informed ML and enterprise AI for clients in real estate, energy, logistics, and utilities. The company bootstrapped profitably for its first year and is now backed by investors in AI, climate, and energy.

About the role

This is a client-facing technical role. You embed with clients, find the outcome behind the request, and turn it into direction for engineers. You act as the technical product manager for your accounts and work with engineers to ship solutions.

A typical day

You might spend the morning with a client in energy or utilities, working out what outcome they actually need. Then you turn that into direction for engineers and work with them on what to build and why. A typical day mixes client calls, engineering decisions, and account-level product calls.

Who AZX, PBC. is looking for

• Can lead engineers and define what to build and why, not just pass along client requests
• Has hands-on technical depth in AI/ML, enough to work with engineers on physics-informed ML and enterprise AI
• Can embed with enterprise clients in energy, utilities, real estate, or logistics and find their real desired outcomes
• Is outcomes-driven and comfortable sitting between client delivery and engineering

Tips for this application

• Name the AZX client categories (real estate, energy, logistics, utilities) and show you understand the problems in at least one of them.
• Give one example where you led engineers and made the build/no-build call, not just coordinated a project.
• Explain how you found the outcome behind a client's stated request and how that changed what was built.
• Show familiarity with physics-informed ML or enterprise AI delivery, since that is what AZX sells.
• Connect your work to climate, decarbonization, climate risk, or energy systems, because that is AZX's focus.

What to cover in your cover letter

The letter should cover: one case where you led engineers and owned the technical product direction; one case where you uncovered a client's real outcome and redirected the work; your hands-on AI/ML depth; and why climate and energy problems are the ones you want to work on.

Research before applying

• Read what AZX means by physics-informed ML and how it differs from standard enterprise AI.
• Look up CBRE, LevelTen Energy, and Flexe and the problems AZX likely solves for each.
• Check AZX's investor base and what focus areas (AI, climate, energy) they bring.
• Find out how AZX splits client delivery and engineering, and ask who you would lead day to day.

Likely interview topics

Based on the job description, expect questions about:

• How you would find the outcome behind a client's stated request and turn it into engineering direction.
• A time you led engineers as a technical product manager: what you decided, what you cut, and what shipped.
• How you would apply physics-informed ML or enterprise AI to a clean energy, decarbonization, or climate risk problem.
• How you would handle a client in energy, utilities, real estate, or logistics whose request does not match their outcome.
• What you know about AZX's clients (CBRE, LevelTen Energy, Flexe) and the problems they bring.

Common mistakes to avoid

• Applying as a pure project manager with no technical depth in AI/ML.
• Describing client work as taking requests and passing them to engineers.
• Showing no knowledge of AZX's focus on climate, energy, or physics-informed ML.