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TL;DR
Precision manufacturing depends on the processes, software, and people behind every build. In this edition of Light Inside, Rudy Bermudez shares how his team brings together software engineering, robotics, AI, and machine learning to support quality at scale.
What is your role at Monarch Quantum?
As Senior Manager of Software Engineering, I oversee two teams that support how our products are built and delivered. Our product software team develops the software that becomes part of the final solution we deliver to customers. Our manufacturing execution software team builds the systems that support hardware production.
Together, we connect product engineering with the processes that make manufacturing repeatable, scalable, and reliable. That includes everything from supporting the manufacturing floor to developing tools our customers and internal teams depend on.
It’s a role that requires both a long-term technical vision and a day-to-day focus on execution.
How do AI and machine learning support advanced manufacturing?
Advanced manufacturing depends on our ability to translate intricate engineering work into repeatable processes and verify quality throughout the build. AI and machine learning are becoming increasingly important tools in that work.
It starts with process development. Our software and robotics teams work closely with optical engineers and physicists to understand tasks such as aligning optics, then translate that expertise into process a robotic system can execute consistently. That requires understanding what to measure, how to interpret it, and when to make an adjustment.
Quality control is closely connected to that process. Machine vision helps us inspect critical steps, such as adhesive curing, while measurements taken during active alignment help guide optical positioning. Applying machine learning to inspection can help us identify problematic components and flag variation earlier in the build.
A large majority of our Quantum Light Engines™ are manufactured using robotics. We are now applying machine learning to computer vision for incoming component inspection and to support more informed decisions at key stages of assembly. The goal is to catch issues early, reduce variability, and build quality into the process.
Our robotics team is also using new tools to create digital twins of our robotic assembly stations. These virtual models allow us to evaluate and refine manufacturing processes before making changes to the physical line, helping accelerate process development and reduce risk.
Within software development, AI is part of our daily workflow. We use it to brainstorm solutions, accelerate coding, and support code review. Our engineers remain responsible for validating the results and making sure the software meets our requirements.
What is the most challenging aspect of what you do?
The pace of change. The tools, models, and techniques available today look dramatically different from what existed even eighteen months ago. Keeping up as a team requires constant learning and a willingness to rethink how we work.
At the same time, we have products to deliver and deadlines to meet. We have to decide where a new tool can improve our work, how to evaluate it, and when to introduce it to an established process.
Balancing immediate needs with investment in the right foundations takes careful judgment. Bringing the team into those decisions is one of the hardest and most rewarding parts of the role.
How do you know when you and your team have been successful?
I know we’re successful when our wok helps everyone else do their jobs well. Software connects how we build, test, and integrate our hardware. When those systems work well, our teams have the tools and information they need to move forward with confidence.
I also measure success by how the team is growing. People taking on new challenges, building skills, and feeling ownership over their work matters just as much to me as a software release. That growth deverything we deliver.
When you aren’t working, what are you doing?
When I’m not at Monarch, you’ll probably find me out on a bike. San Diego is a cyclist’s dream, and there’s nothing quite like a long ride to reset the mind. When I’m not on two wheels, I’m most likely tucked into a corner of one of my favorite cafés, working my way through a good book. It’s a nice counterbalance.