Senior Algorithm Engineer (Phase Retrieval)

Aim Solutions; Netherlands; On-site; EUR 90,000 - 130,000; Full time

Benefits offered by this job

Ownership of reconstruction algorithms

Small team, fast feedback

Competitive Dutch salary

Job summary

AIM Solutions is building an advanced GPU-accelerated metrology tool in Eindhoven. You will own the core algorithms for phase retrieval and wafer metrology, shaping the frontier of computational imaging.

This senior role blends physics, optimization, and production-grade software.

Qualifications

• PhD or equivalent research depth in a physics-heavy or numerically heavy field.
• Several years developing numerical or scientific algorithms, ideally in imaging or inverse problems.
• Strong numerical optimization, both gradient-based and iterative methods.
• Working knowledge of Fourier optics and wave-propagation physics.
• Strong Python and array programming with GPU frameworks (JAX, PyTorch, TensorFlow, CUDA).
• You write tested, maintainable code that others can build on.
• You can reason from physics to algorithm and back to improve reconstructions.
• Full professional proficiency in English.

Responsibilities

• Develop and maintain GPU-accelerated computational imaging engine for phase retrieval.
• Improve reconstruction quality, convergence, and robustness across varied datasets.
• Design and implement new reconstruction methods with physics-based models and regularization for ill-posed problems.
• Translate methods from literature and internal ideas into tested, performant code.
• Use physical insight into the optical system to steer algorithm improvements.
• Profile and optimize the engine for GPU performance on large datasets.

Skills

Numerical optimization

Python

GPU programming

Fourier optics

GPU frameworks

Code testing

Physics-to-algorithm reasoning

Education

PhD or equivalent

Tools

CUDA

JAX

PyTorch

TensorFlow

Job description

About us

AIM Solutions is an early-stage deep-tech startup in Eindhoven building inspection and metrology equipment for the semiconductor industry. We are in active build phase, small, and growing.

The role

You will own the algorithms at the core of our instrument: the GPU-accelerated engine for phase retrieval and wafer metrology. This is the deep end of computational imaging, where progress comes from understanding both the optimization and the physics.

Responsibilities

• Develop and maintain our GPU-accelerated computational imaging engine for phase retrieval
• Improve reconstruction quality, convergence, and robustness across varied and imperfect datasets
• Design and implement new reconstruction methods: physics-based differentiable forward models, regularization and constraint schemes for ill-posed problems, and support for new acquisition modes
• Translate methods from the literature and from our own ideas into tested, performant code
• Use physical insight into the optical system to decide how to change the algorithm so that results improve
• Profile and optimize the engine for GPU performance on large datasets

This is a senior, research-to-production role. We expect a track record of taking new methods from concept to working, maintained code that runs in a real instrument.

Your profile

A PhD, or equivalent research depth from industry, in a physics-heavy or numerically heavy field

Several years developing numerical or scientific algorithms, ideally in imaging, inverse problems, or a comparable domain

Strong numerical optimization, both gradient-based and iterative methods

Working knowledge of Fourier optics and wave-propagation physics

Strong Python and array programming, with experience in GPU frameworks such as JAX, PyTorch, TensorFlow, or CUDA

You write tested, maintainable code that others can build on

You can reason from physics to algorithm and back, and judge what will improve a reconstruction

Technical curiosity; we will teach you the specifics of our computational imaging approach

Full professional proficiency in English

Bonus experience

• Phase retrieval, coherent diffractive imaging, holography, or tomography
• Physics-informed machine learning, differentiable forward models, automatic differentiation
• Regularization and constrained optimization for ill-posed inverse problems
• High-performance and GPU computing with performance profiling
• Linux development environments

What we offer

• Ownership of the reconstruction algorithms at the heart of a tool that does not exist anywhere else yet
• Small team, short feedback loops, an open environment to have real impact
• Competitive salary for the Dutch market