Job Description

Location: Redmond, WA

Duration: 12 Months

The client goal is to explore, innovate and design Client 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 research engineer with expertise in probabilistic programming to accelerate the team’s research.

The role will include evaluating and implementing models in various probabilistic programming languages (e.g Gen/Julia, Pyro, Stan), baseline published inverse reinforcement learning, inverse planning, and cooperative reinforcement learning algorithms; collaboration and experimentation on Client 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:

2+ years experience in probabilistic programming, inverse reinforcement-learning, dynamic programming, active/online learning, Markov decision processes or related areas.

● Minimum of 2 years experience with 1 or more probabilistic programming languages (e.g. Stan, Church, WebPPL, Gen), Julia and/or Python (SciPy and other related packages)

Preferred qualifications

● Experience with at least one deep learning toolkit (e.g., PyTorch or TensorFlow)

● Experience working in shared codebases and cluster computing environments

Educational background

Required: BS in computer science, cognitive science, statistics, robotics, or a related field.

Preferred: MS or PhD in computer science, cognitive science, statistics, robotics, or a related field.