Inference Algorithm Engineer

About this vacancy

As an Inference Algorithm Engineer, you develop the algorithms and mathematical frameworks that enable high‑tech systems to extract structure, estimate states and reason under uncertainty in high‑dimensional environments. You work where applied physics, probabilistic modelling, numerical optimisation and machine learning intersect.

Your responsibilities:

• Develop physics‑informed inference algorithms grounded in probability theory, system dynamics and measurement physics.
• Build mathematical models describing complex physical systems, including noise processes, interactions and uncertainty mechanisms.
• Design Bayesian estimation pipelines such as Kalman filtering variants, particle filters, variational inference or MCMC methods.
• Prototype, validate and benchmark algorithms through simulation and experimental setups.
• Translate theoretical models into efficient, scalable and real‑time capable implementations.
• Collaborate with sensing specialists, physicists, embedded software engineers, control experts and system architects to integrate inference algorithms end‑to‑end.
• Perform sensitivity studies, robustness analysis and uncertainty propagation assessments across physical models.
• Explore new physics‑informed inference methodologies and identify opportunities for performance breakthroughs.
• Present clear, well‑founded algorithmic decisions and guide multidisciplinary teams in selecting the right inference approach.

What we expect from you

As an Inference Algorithm Engineer, you develop the algorithms and mathematical frameworks that enable high‑tech systems to extract structure, estimate states and reason under uncertainty in high‑dimensional environments. You work where applied physics, probabilistic modelling, numerical optimisation and machine learning intersect.

Your responsibilities:

• Develop physics‑informed inference algorithms grounded in probability theory, system dynamics and measurement physics.
• Build mathematical models describing complex physical systems, including noise processes, interactions and uncertainty mechanisms.
• Design Bayesian estimation pipelines such as Kalman filtering variants, particle filters, variational inference or MCMC methods.
• Prototype, validate and benchmark algorithms through simulation and experimental setups.
• Translate theoretical models into efficient, scalable and real‑time capable implementations.
• Collaborate with sensing specialists, physicists, embedded software engineers, control experts and system architects to integrate inference algorithms end‑to‑end.
• Perform sensitivity studies, robustness analysis and uncertainty propagation assessments across physical models.
• Explore new physics‑informed inference methodologies and identify opportunities for performance breakthroughs.
• Present clear, well‑founded algorithmic decisions and guide multidisciplinary teams in selecting the right inference approach.

What you can expect from us

TMC is an international high-tech consultancy company that brings expertise to their clients in order to support them with the realization of their technological challenges. As such, TMC is operating as an expert center at the disposition of its clients and we help them to be more competitive in an ever more global and faster changing technological world. At TMC we believe that people are the driving force behind technological innovation. That is why we want to create the best possible conditions for tech talents to thrive in. We offer you a challenging and stimulating work environment in which you can be the director of your own career. As an Employeneur you are part of our TMC family. Next to our outstanding technical expertise, fun and engagement are meaningful parts of our culture.