Disys
Research Analyst II
– Probabilistic programming for user models
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At Reality Labs, our goal is to explore, innovate and design novel interfaces and hardware for the next generation of virtual, augmented, and mixed reality experiences.
We are driving research towards a vision of an always-on AR device that can provide contextually relevant assistance across a range of complex, dynamic, real-world tasks in natural environments.
To this end, we are looking for a computational cognitive science research analyst with background in reinforcement learning (RL) who can accelerate the team’s research.
The role will include evaluating and implementing models in various probabilistic programing languages (e.g Gen/Julia, Pyro, Stan), baseline published inverse reinforcement learning, inverse planning and cooperative reinforcement learning algorithms; collaboration and experimentation on novel algorithms and models with researchers; collaboration with engineers to deploy models in AR/VR prototypes; and other related work.
Minimum qualifications
● Experience in one or more of the following areas: Probabilistic programming, computational models of human perception/cognition or perceptual-motor control, inverse reinforcement-learning, optimal control, dynamic programming, active/online learning, Markov decision processes, human-machine collaboration, or related areas.
● Minimum of 2 years experience with Python (SciPy and other related packages), R and/or Julia
Preferred qualifications
● Experience with at least one deep learning toolkit (e.g., PyTorch or TensorFlow)
● Familiarity with approaches to multi-agent systems, meta-learning, and online learning methods.
● Experience working in shared code bases and cluster compute environments
Educational background
Required: BS in cognitive science, computer science, statistics, robotics, or a related field.
Preferred: MS or PhD in cognitive science, computer science, statistics, robotics, or a related field.