Algorithms engineer (Optimization & ML)

About Invisix

Semiconductor manufacturers can't build what they can't measure. Invisix Measuring Systems is a fast-growing scale-up in the high-tech industry, developing next-generation metrology solutions for the semiconductor sector. We work at the intersection of optics, software, and data.

We're a small team, but the depth of knowledge inside Invisix is anything but small. Our culture is professional and fast-paced, with short communication lines and a strong sense of shared purpose. Everyone here is committed, and everyone knows their work directly contributes to something bigger.

The Role

We're looking for an Algorithms Engineer to develop the algorithms that turn data from our metrology equipment into reliable measurement results: modelling, numerical optimization, machine learning and validation amongst others.

As a hands-on individual contributor in our algorithm engineering team, you'll choose the approach the problem calls for, build it, and prove that it holds up on real data. You'll work closely with the software team to get your algorithms into production, writing code that is tested, maintainable and runs at scale on our HPC infrastructure.

Key Responsibilities

• Design, implement and validate algorithms for parameter-estimation problems
• Build and refine the models that describe our measurements
• Parallelize and scale algorithms across our HPC infrastructure
• Validate algorithms against real, noisy measurement data, and reason rigorously about their failure modes
• Write production-ready Python code in close collaboration with other developers, following the team's engineering standards

Is this you?

• PhD (or MSc with equivalent applied experience) in a quantitative field such as applied mathematics, physics, electrical engineering or computer science
• Hands-on experience developing algorithms in one or more of these areas: inverse problems, numerical optimization, machine learning, data fitting, or validating algorithms in real-world conditions
• Strong, current Python engineering skills, and comfortable working with multiple developers on production-ready code
• Comfortable validating an algorithm or model against real, noisy data and reasoning rigorously about its failure modes
• Active use of AI tooling in your daily work (coding, testing, experimentation, documentation) to boost efficiency

Nice to have

• Experience working with physical models or simulation, which is a significant advantage
• A background in HPC, GPU or parallel computing
• Experience turning research code into a maintained, tested pipeline with CI, including basic MLOps practices such as dataset and model versioning and reproducible runs

What We Offer

• A role at the heart of Invisix's algorithm development, with real technical ownership from day one
• Competitive salary and benefits, including an Employee Share Ownership Plan
• A learning and development budget to support your growth
• Flexible working arrangements, and room to grow into adjacent responsibilities as the team scales

Interested?

If this sounds like your kind of challenge, we'd like to hear from you, even if you only recognize yourself in part of this description. Submit your resume, and let's find out together if there's a match.

If this isn't quite the right position for you but you're still interested in Invisix, reach out to us at [email protected].

Diversity & Inclusion

We're committed to a diverse and inclusive workplace, where ideas and individuals flourish. We welcome applicants from all backgrounds and judge candidates on merit and potential contribution, regardless of race, ethnicity, gender, sexual orientation, age, religion, disability, or other identity characteristics. What we're looking for is talent with integrity, a collaborative mindset, and a drive to make an impact.

Your Data

Your application data is processed by Invisix and kept in our applicant tracking system so we can consider you for this role and future opportunities. See our privacy policy for details: invisix.com/privacybeleid/