R&D Scientist – Probabilistic Machine Learning & Bayesian Inference - #2076331

Job Description

Vision being the dominant human sense, eye tracking constitutes a powerful approach for understanding the human mind! At Pupil Labs, our mission is to provide cutting-edge eye-tracking solutions, which are more robust, accurate, accessible, and user-friendly than ever before. Already today, our products empower thousands of users in academia and industry, clinical surgeons, elite athletes, astronauts on the International Space Station, and many more. Unlocking the full potential of eye-tracking technology relies on solving hard research problems, ranging from core gaze-estimation algorithms to developing cloud-based algorithmic tools allowing for the high-level analysis of terabytes of egocentric video data.

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.

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