Meta AR/VR Job | Research Engineering Intern, Audio Machine Learning (PhD)

Job(岗位): Research Engineering Intern, Audio Machine Learning (PhD)

Type(岗位类型): Data Engineering | Engineering, Research

Citys(岗位城市): Sunnyvale, CA

Date(发布日期): 2022-1-18

Summary(岗位介绍)

Meta Reality Labs TED Audio has centralized audio functions such as transducers, acoustics, devices and machine learning based audio algorithms. The team is focused on algorithm development and commercialization of real world products, with responsibilities to end to end audio experience. Current team consists of mostly Ph.D engineers and scientists working on Deep learning and machine learning algorithms for AR/VR products. As an intern in our team you will have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale.

Our internships are twelve (12) to sixteen (16) weeks long and we have various start dates throughout the year.

Qualifications(岗位要求)

Currently has, or is in the process of obtaining a PhD in Electrical Engineering, Computer Science, Computer Engineering, Applied Mathematics, or related field

Completed coursework on signal processing, image processing or signal and systems

Hands-on experience with one or more deep learning/ML frameworks such as Tensorflow, PyTorch or Keras

Strong programming skills in Python and/or C/C++

Interpersonal skills: cross-group and cross-culture collaboration

Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment

Description(岗位职责)

Explore and research new and emerging machine learning based audio algorithm development for AR/VR products.

Work closely with ML/AI engineers and Audio algorithm scientists on productizing research algorithms.

Additional Requirements(额外要求)

Intent to return to degree-program after the completion of the internship/co-op

Research publications in conferences or journals

Experience with developing audio audio signal processing algorithms such as noise suppression, acoustic echo cancellation, sound field classifications or machine learning algorithms

Knowledge of training data augmentation and large dataset preparations

Experience with real-time systems. Experience in working with GPU, cloud systems, low-level optimizations, CuDNN

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