Research Scientist Intern, Multimodal AI; PhD

Position: Research Scientist Intern, Multimodal AI (PhD)
The Meta Reality Labs Research Team brings together a world-class team of researchers, developers, and engineers to create the future of virtual and augmented reality, which together will become as universal and essential as smartphones and personal computers are today. And just as personal computers have done over the past 45 years, AR, VR and MR will ultimately change everything about how we work, play, and connect.

We are developing all the technologies needed to enable breakthrough AR glasses and VR headsets, including optics and displays, computer vision, audio, graphics, brain-computer interfaces, haptic interaction, eye/hand/face/body tracking, perception science, and true telepresence. Some of those will advance much faster than others, but they all need to happen to enable AR, VR and MR that are so compelling that they become an integral part of our lives.

In particular, the Meta Reality Labs Research audio team is focused on two goals; creating virtual sounds that are perceptually indistinguishable from reality, and redefining human hearing. See more about our work here:
Inside Facebook Reality Labs Research:
The future of audio and Filter Out the Noise With Conversation Focus. These two initiatives will allow us to connect people by allowing them to feel together despite being physically apart, and allow them to converse in even the most difficult listening environments. Meta Reality Labs Research is looking for experienced interns who are passionate about ground breaking research in audio signal processing, machine learning and audio visual learning to solve important audio-driven problems for AR/VR applications.

We currently have open positions for a range of projects in multimodal representation learning, audio visual scene analysis, egocentric audio visual learning, multi-sensory speech enhancement and acoustic activity localization. Our internships are twelve (12) to twenty four (24) weeks long and we have various start dates throughout the year.

Minimum Qualifications

* Currently has, or is in the process of obtaining, a PhD degree in the field of Computer Science, Artificial Intelligence, Generative AI, Transformer Models, Machine Learning, Signal Processing or Computer vision

* 3+ years experience with Python, Matlab, or similar

* 3+ years experience with machine learning software platforms such as PyTorch, Tensor Flow, etc

* Experience building novel audio computational models and LLM

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

Preferred Qualifications

* Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. Github)

* Experience in advancing AI techniques, including core contributions to open source libraries and frameworks in computer vision or audio processing

* Experience with audio and speech quality assessment

* Experience with multichannel audio processing

* Experience in visual and acoustic scene analysis

* Experience manipulating and analyzing complex, large scale, high-dimensionality data from varying sources

* Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or top computer vision and machine learning conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, ICCV, ECCV, ICASSP, Inter Speech or similar

* Experience in utilizing theoretical and empirical research to solve problems

* Experience working and communicating cross functionally in a team environment

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

Responsibilities

* Design, implement, and maintain comprehensive evaluation protocols for large language models, including both automated and human-in-the-loop assessments.

* Develop and curate high-quality datasets and benchmarks to measure model performance, safety, fairness, and robustness across a variety of tasks and modalities.

* Analyze model outputs to identify strengths, weaknesses, and failure modes, and provide actionable…