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Glacierware
Computational Optimisation Engineer
About Corintis
Corintis offers innovative microfluidic cooling technologies for AI chips/GPUs and CPUs used in data centres. Working with many of the world’s largest tech companies, our solutions improve compute sustainability and tackle the excessive electricity consumption associated with data centre cooling, which consumes more electricity than New York and London combined.
Ranked as the No.1 Engineering startup in Switzerland for 2025, Corintis offers a friendly and team-oriented workplace, bringing together over 105 people from over 35 nationalities to solve the most significant computing challenges of tomorrow. Based in the EPFL Campus in St. Sulpice, we are closely connected to the local ecosystem and are located a few minutes walk from Lake Geneva.
The Role
As a Computational Optimization Engineer, you will build the optimization technology that defines the internal geometry of our cooling products. You will report to the Head of Software Engineering and work closely with the computational, product, and manufacturing teams.
The performance of a cold plate depends on the shape of the microstructures inside it. Your algorithms select that shape. Better designs give a lower thermal resistance, a lower pumping power, and more compute per watt for our customers. You will move each method from the mathematical formulation to a tested module that our engineers use every day.
Key Responsibilities
- Develop and implement numerical methods for topology optimization, PDE-constrained optimization, and multiscale material design.
- Formulate and solve optimization problems that involve homogenization, effective material properties, and microstructure-driven performance.
- Design, implement, and maintain simulation and optimization software in Python and/or C++.
- Build computational workflows that combine finite element analysis, sensitivity analysis, adjoint methods, and gradient-based optimization.
- Apply homogenization techniques to characterize and optimize lattices, composites, and architected microstructures.
- Translate engineering and scientific requirements from other teams into computational models and optimized designs.
- Verify and validate the numerical models, the optimization algorithms, and the simulation results.
- Document the methods, the code, and the results, and present the outcomes to technical and non-technical colleagues.
- Monitor the advances in topology optimization and computational mechanics, and introduce the applicable ones into our software.
Requirements
- Ph.D. in Mechanical Engineering, Aerospace Engineering, Civil Engineering, Computational Engineering, Applied Mathematics, Materials Science, Physics, or a related field.
- Expertise in topology optimization, such as density-based methods, level-set methods, or shape optimization.
- Experience with homogenization methods, multiscale modeling, and the computation of effective material properties.
- Solid background in the finite element method and in numerical methods for partial differential equations.
- Experience with sensitivity analysis, adjoint methods, and gradient-based optimization.
- Proficiency in Python and/or C++ for reliable scientific software.
- Knowledge of software development practices: version control, testing, documentation, and modular code design.
- Experience with high-performance computing and parallel programming, preferably with MPI.
- Fluent English, with strong written and verbal communication skills.
This Is a Great Fit If You
- Enjoy difficult mathematical problems, and you want to see them run in a production code base.
- Are motivated by physical impact, because the geometry your solver selects becomes hardware.
- Thrive when you own a method from the first derivation to the tested and documented module.
- Value numerical accuracy, code review, and reproducible results.
- Like to run several technical projects in parallel and to deliver them on schedule.
- Prefer a small team on site, with a short path from an idea to a measurement.
This Won’t Be the Right Role for You If:
- You prefer to apply existing solvers, and you do not want to implement numerical methods yourself.
- You look for a purely academic position, without software delivery and without product deadlines.
- You prefer a large corporate environment with fixed processes.
- You prefer fully remote work, because this role needs on-site collaboration.
Department Glacierware Locations Lausanne Employment type Full-time
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Glacierware
Computational Optimisation Engineer
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