R&D Scientist – Probabilistic Machine Learning & Bayesian Inference

The interdisciplinary R&D team at Pupil Labs, comprising members with backgrounds inComputer Science, Computational Neuroscience, Mathematics, and Physics, is tackling these challenges head-on! In close collaboration with other engineering teams, we identify promising R&D avenues and take pride in seeing our results swiftly integrated into the latest products shipped to our customers.

To support our efforts, we are looking to grow our R&D team in Berlin with afull-time R&D Scientist with expertise in probabilistic machine learning and Bayesian inference. This is anon-site position(with up to two home-office days per week).

Pupil Labs offers a competitive salary, flexible work arrangements, a great team of coworkers, a young and dynamic company structure, and a culture of participation and feedback.

Are you excited about joining an ambitious, international, diverse, interdisciplinary, young, enthusiastic, and talented team of researchers and software specialists? Do you have a growth mindset, thrive in fast-paced work environments, and enjoy working on hard problems? Then we are looking forward to hearing from you!

What you would do

• Develop and applyBayesian inference methodsto buildprobabilistic models for eye-tracking and physiological data.
• Design and implementgenerative models, includingenergy-based models, normalizing flows, and diffusion modelsfor state estimation and posterior sampling.
• Work withuncertain, noisy dataand developrobust methods for inference, estimation, and uncertainty quantificationin the field of ocular research.
• Implement and optimizescalable probabilistic algorithmsthat can be deployed in real-time or large-scale analysis settings.
• Collaborate with our research and engineering teams to bringadvanced probabilistic modeling techniquesinto real-world eye-tracking applications.

Who you are

• You hold aPhDinmachine learning, statistics, applied mathematics, physics, or a related field.
• You have strong expertise inBayesian machine learningandprobabilistic modeling.
• You have experience withsampling techniquessuch asMCMC, Langevin dynamics, Hamiltonian Monte Carlo (HMC), or variational inference.
• You are proficient inPythonandPyTorch.
• You have experience withgenerative models, includingnormalizing flows, energy-based models, and diffusion models.
• You are comfortable working withuncertain and high-dimensional dataand developing methods foruncertainty quantification.
• Ideally, you have experience withoptimization techniques for probabilistic models, contrastive divergence, or physics-inspired ML.
• You are self-motivated, enjoy working in an interdisciplinary setting, and are comfortable inwritten and spoken English.

Perks

• Abeautiful officein the heart of Berlin.
• Up totwo home office days per week.
• 15 mobile office days per year.
• Continued learning and professional development(we will sponsor you to attend relevant scientific/developer conferences.
• Flexible working hours.
• Publishing of scientific articles.
• 6 weeks of holidays per year.