Job ID 124687
The Reality Labs hardware teams at Meta build our VR, AR, Glasses, and Wearable devices. The potential of these products to connect the world is immense—and we’re just getting started. Come join the team to ship emerging technologies to hundreds of millions, driving the state-of-the-art forward with breakthrough work in physical acoustic, Audio algorithm, Audio Experience Optimization, and advanced audio technology development, to deliver mixed and virtual reality experiences as part of our family of products to enable the Metaverse.We are currently hiring an experienced Audio Algorithm Technology Manager to develop technology roadmaps, influence product roadmaps, align tech development with future product requirements, represent the technology teams with our cross-functional partners, and deliver innovative technologies that support the RL product vision. Successful candidates must be comfortable working in a dynamic cross-functional environment, and exhibit high signal-to-noise synthesis (pun intended) abilities and communication.
Manager, Audio Engineering Responsibilities:
- Lead a team of DSP and Audio ML engineers to develop World Class Audio and Enhancement algorithms for voice communications, Always On Assistant, and music enhancement in MATLAB, floating Point, and Fixed Point languages. Lead the fusion of embedded small footprint machine learning and classical algorithm fusion for advanced applications in speech enhancement, signal separation, and multi modal sensor fusion applications running on the edge. Develop new algorithms, User experiences, and features for next generation communications, virtual reality and augmented reality experiences. Build and manage a multi-generational technology and audio experience roadmap. Define, design, develop and debug real-time audio system software for forward looking products and user experiences. Develop a forward looking technology shelf and dive into new areas with unfamiliar technologies. Identify, evaluate and drive third party algorithm partners. Generate novel, innovative solutions to some of the toughest problems in audio. Coaching, mentoring, and developing a team of engineers. Champion a data driven culture, operational excellence, and establishing processes and operational metrics and KPIs. Communicate and collaborate effectively with cross-functional HW and SW engineering teams. Build and develop an effective team. Support recruiting and staffing efforts. Partner with academia and industry partners on new innovation opportunities.
- Experience with technical leadership and demonstrated success through data managing people. 7+ years experience managing embedded DSP SW teams and audio algorithm development. Experience with ML applications in Audio domain such as noise reduction, scene classification, and signal separation. Demonstrated understanding of how end-to-end audio systems work including transducers, audio ICs, acoustics, embedded DSPs, in mobile and embedded platforms. 10+ years experience creating efficient real-time embedded audio algorithms. Demonstrated communication skills and experience working across disciplines to drive optimal solutions and supporting cross functional teams. Experience with porting and optimizing C code to a target platform. Familiarity with various DSP platforms and the trade-offs of embedded implementations. Experience in any of the following areas: Echo Controls, Multi mic noise reduction, Speech Intelligibility Enhancement, Microphone beam forming, ML, Blind Source Separation, Signal Separation. Experience in developing AGC, Multi band compression, HRTF Surround Sound, Speaker Array processing, Speaker protection. BS degree in Computer Science or related field, or equivalent experience.
- MS or PhD in Electrical Engineering, Computer Science or equivalent. 10+ years of Fixed Point and Floating point Audio Algorithm development into embedded DSP and ARM cores. Experience in Acoustics and Consumer Audio EE Architecture. Experience shipping millions of embedded audio DSP products. Experience with embedded Low Footprint machine learning and application of machine learning techniques to voice enhancement and music enhancement algorithms. Experience with machine learning for Audio Scene Classification, Always on Wakeword and voice interaction.
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