At Meta's Reality Labs Research, our goal is to make world-class consumer virtual, augmented, and mixed reality experiences. Come work alongside industry-leading scientists and engineers 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 a world-class team of researchers and engineers creating the future of augmented and virtual 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 and VR will ultimately change everything about how we work, play, and connect.We are developing all the technologies needed to enable breakthrough Smart glasses, 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 and VR that are so compelling that they become an integral part of our lives.The Audio team within RL Research is looking for a Research Engineer with experience in speech and audio natural language processing, and large language models. You will be doing applied research and applied engineering in technologies that improve listener's hearing abilities using wearable computing, and alongside a team of dedicated research scientists, developers, and engineers. You will operate at the intersection of large-scale machine learning systems design and development, and speech, hearing/audio health and hearing impairment technologies. You'll also contribute to novel egocentric audio-visual learning, acoustics, computer vision, and signal processing algorithms with hardware and software co-design.

• Design and implement large-scale benchmarking of natural language ML models for speech, voice and audio representation models.
• Design and test novel data generation tools, data processing and quality checking setups for building novel speech and audio based LLMs.
• Support ML models integration and real-time online testing on research & development platforms for wearables.
• Support quick prototyping, proof of concept, or proof-of-experience and demonstrations via the real-time integration of ML models into research & development platforms for wearables.
• Contribute to technical directions on novel ML and DSP research supporting text and language models design capturing speech, audio and voice datasets supporting egocentric wearable computing in AR and VR.

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• Master's degree or equivalent experience in Computer Sciences, Computer Engineering, Artificial Intelligence, Machine Learning, Robotics, Computer Vision, Computational Neuroscience, Signal Processing, Speech and Language technologies, or a related field.
• 3+ years of experience working on applied machine learning and natural language processing / understanding.
• 2+ years of experience evaluating, fine-tuning and benchmarking large language models (like GPT, LLamas etc).
• Research-oriented software engineering skills, including fluency with machine learning (e.g., PyTorch, TensorFlow, Scikit-learn, Pandas) and libraries for scientific computing (e.g. SciPy ecosystem).
• Experience of building collaborative relationships that lead to impact
• Experience with architectural and system engineering on large scale software applications.

• PhD degree or equivalent experience in Computer Sciences, Computer Engineering, Artificial Intelligence, Machine Learning, Robotics, Computer Vision, Computational Neuroscience, Signal Processing, Speech and Language technologies, or a related field.
• 2+ years of experience in speech, hearing and audio perception research & technology development.
• 4+ years of experience working on applied machine learning and natural language processing / understanding, specifically on novel language models design, training, evaluating and benchmarking.
• Experience in datasets and language prompts design for fine turning large language models.
• Experience in system optimization and hyperparameter tuning for improving language prompts based Assistants or recommender systems.