Process Engineer

We are building Chladni, an industrial intelligence platform focused on improving yield and eliminating waste in highly regulated manufacturing environments, starting with pharmaceutical production. Our goal is to move beyond reactive quality control and toward real-time process understanding, early deviation detection, and continuous process optimization.

In pharmaceutical manufacturing, waste often comes from late detection of process drift, variability that goes unnoticed until batch release, and limited visibility into what is actually happening inside the process. We believe PAT, when done properly, is one of the most powerful tools to eliminate this waste and improve yield without compromising compliance.

We are looking for a PAT Engineer or Scientist to help design and guide the process intelligence layer of our system for pharmaceutical manufacturing. This role is focused on real production environments, not academic demonstrations.

You will help define how process signals are selected, interpreted, validated, and translated into actionable insights that improve yield, reduce batch failures, and support regulatory compliance.

What You Will Work On

You will work on building systems that monitor pharmaceutical processes in real time, identify early signs of deviation, and distinguish normal variability from true process risk. You will help define how PAT data is used to reduce waste, improve consistency, and increase right-first-time production. You will collaborate with data scientists, software engineers, and domain experts to ensure that insights are scientifically sound, operationally useful, and regulator-friendly.

This role sits at the intersection of process engineering, data analytics, and quality-by-design.

Who This Role Is For

This role is right for someone who has hands-on experience with PAT in pharmaceutical manufacturing and understands how processes actually fail in the real world. You should be comfortable with process data, multivariate analysis, and continuous monitoring concepts. Experience with spectroscopy, process sensors, soft sensors, or multivariate statistical process control is highly relevant.

You do not need to be a hardcore software engineer, but you should be comfortable working with data-driven systems and collaborating with ML and backend teams. You should understand GMP environments and how to design systems that support, not fight, regulatory expectations.

Is This the Right Role for You?

This is the right role if you care about improving yield by eliminating waste at the process level, not by adding more inspections at the end. It is not the right role if your experience is purely academic or limited to writing validation reports without owning real process outcomes.

If you are motivated by building systems that help pharmaceutical plants run smarter, cleaner, and more efficiently — while staying compliant — this role is for you.