Application Guide

How to Apply for ML Engineer (Senior/Staff)

at AZX, PBC.

🏢 About AZX, PBC.

AZX, PBC is a profitable public benefit corporation founded in 2024, specializing in physics-informed ML and enterprise AI for climate and sustainability. They work with industry leaders in real estate, energy, logistics, and utilities, and are committed to long-term positive impact.

About This Role

As an ML Engineer, you'll own the technical backbone of model serving and evaluation at scale. This includes GPU scheduling, autoscaling, and serving infrastructure for vLLM/SGLang, as well as building evaluation systems to measure model and agent improvements. Your work directly enables reliable AI deployment for critical industries.

💡 A Day in the Life

A typical day might involve collaborating with ML researchers to understand new model requirements, then designing and implementing changes to the serving infrastructure. You'd also monitor system performance, analyze evaluation results to guide prompt/model updates, and participate in code reviews and architecture discussions with the team.

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Interview Prep

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🎯 Who AZX, PBC. Is Looking For

  • Deep experience with ML infrastructure, specifically serving frameworks like vLLM or SGLang and GPU orchestration (e.g., Kubernetes, Ray).
  • Strong software engineering skills in Python and familiarity with cloud platforms (AWS, GCP, Azure) and on-premise deployment.
  • Proven ability to design and implement evaluation pipelines for LLMs, including A/B testing and metrics tracking.
  • Understanding of physics-informed ML or a strong interest in climate/energy applications, with ability to collaborate with domain experts.

📝 Tips for Applying to AZX, PBC.

1

Tailor your resume to highlight specific projects where you built or optimized serving infrastructure (e.g., latency reduction, autoscaling) and evaluation systems.

2

Mention any experience with vLLM, SGLang, or similar inference engines explicitly, with metrics like throughput and GPU utilization improvements.

3

Showcase your impact on business outcomes, not just technical details; quantify results (e.g., 'reduced inference cost by 30%').

4

Since AZX is a PBC, emphasize your passion for climate/sustainability and how your skills can accelerate their mission.

5

Include links to your GitHub, blog, or talks that demonstrate your expertise in ML infrastructure and evaluation.

✉️ What to Emphasize in Your Cover Letter

['Your experience with high-scale model serving and your architectural approach to reliability and efficiency.', 'Your ability to build evaluation systems that drive decision-making for model and prompt changes.', "Your enthusiasm for applying AI to climate and sustainability challenges, and why AZX's mission resonates with you.", 'How you collaborate with cross-functional teams (ML researchers, engineers, domain experts) to deliver robust solutions.']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read AZX's website and blog to understand their focus on physics-informed ML and their client case studies (e.g., with CBRE, LevelTen Energy).
  • → Look up their team on LinkedIn to understand their backgrounds and culture.
  • → Research recent trends in LLM serving (vLLM, SGLang) and evaluation (e.g., LLM-as-a-judge, RAGAS) to speak to current best practices.
  • → Understand the public benefit corporation structure and how AZX balances profit with positive impact.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Design a scalable inference platform for serving multiple LLMs with varying traffic patterns; discuss trade-offs between vLLM and SGLang.
2 How would you implement an evaluation framework to compare prompt changes across a suite of tasks? Include metrics and guardrails.
3 Describe a time you debugged a GPU memory leak or performance bottleneck in a production system.
4 How do you ensure high availability and low latency for models deployed across cloud and customer-managed clusters?
5 What is your experience with Kubernetes and autoscaling strategies for GPU workloads? Provide examples.
Practice Interview Questions →

⚠️ Common Mistakes to Avoid

  • Avoid generic cover letters; make sure to mention AZX's mission and specific technical requirements.
  • Don't overlook the evaluation aspect; many candidates focus only on serving and miss the importance of evaluation systems.
  • Don't be vague about your experience; provide concrete examples with numbers and outcomes.
  • Don't forget to research the company; not knowing their clients or PBC status signals lack of interest.

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to AZX, PBC.!

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