Research Scientist, Neural Networks (PhD University Grad)

Research | Menlo Park, CA - Redmond, WA

Oculus is the world leader in the design of virtual and augmented reality systems. Come work alongside expert engineers and research scientists to create the technology that makes VR and AR pervasive and universal. Join the adventure of a lifetime as we make science fiction real and change the world. We are seeking a Research Scientist to support development of state-of-the-art deep learning hardware components optimized for AR/VR systems. The successful candidate will be part of our efforts to architect, design and implement the hardware platforms for this activity and will be part of a team that includes algorithm, user experience, software, firmware and ASIC experts. The ideal candidate will understand the full stack from algorithms and architecture down to hardware accelerator blocks. This is a full-time position based in either our Redmond, WA or Menlo Park, CA offices.


  • Enable new user experiences in AR/VR via innovative applications of deep learning techniques for body tracking, user interface and other use-cases
  • Develop a system hardware design that includes camera image processing, neural nets and custom compute processing blocks which will surpass state-of-the-art metrics for compute resources, DRAM bandwidth and power consumption
  • Work with algorithm research teams to map CNN graphs to hardware implementations, model data-flows, create cost-benefit analysis and estimate silicon power and performance
  • Support all phases of Silicon SoC development from a deep learning perspective - from early definition on through specification, architecture, layout and production
  • Work with other groups to produce an FPGA test platform to test, develop and optimize the full system
  • Contribute to execution of our silicon technology/compute roadmap to make advances in performance, power consumption and form factor
  • Assess and recommend emerging technologies through partnerships with external suppliers
  • Employ the scientific method to evaluate performance and to debug, diagnose and drive resolution of cross-disciplinary system issues

Minimum Qualifications

  • Currently has or is in the process of obtaining a PhD degree or completing a postdoctoral assignment in the field of Machine Learning, Artificial Intelligence, Computer Vision or similar
  • Available to start employment on or after March 1, 2018
  • Experience in mobile SoC low-power design and architecture methodologies
  • 1+ years hands-on experience in deep learning algorithms and techniques, e.g., convolutional neural networks (CNN), recurrent networks (RNN) and/or related areas
  • 4+ years of experience with C/C++ for development, debugging, testing and performance analysis
  • Knowledge of custom SoC design especially it relates to integration of hardware IP blocks, on-chip buses, DRAM bandwidth and power constraints
  • Experience in real-time processing for computer vision and user interaction tasks, high-compute/throughput systems and using simulation and modeling technique to estimate performance and power
  • Interpersonal experience: cross-group and cross-culture collaboration
  • Obtain work authorization in the U.S. beginning in 2018

Preferred Qualifications

  • 3+ years of experience in digital IC design
  • Experience with Python and MATLAB
  • Experience implementing CNN for low-power SoC
  • Knowledge of industry trends and technologies for optimizing CNNs to reduce DRAM bandwidth requirement, on-chip storage and compute requirements
  • Experience with embedded DSP and GPU architectures
  • Experience with EDA design tools and instruction set simulators
  • Hands-on experience with FPGA and vendor specific hardware evaluation boards
  • Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as ISSCC, ISCAS, ICML, CVPR, or similar

Ready to Join?

Apply Now

Oculus is proud to be an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, genetic information, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law.

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