Research Engineer - Machine Learning

Research Engineer - Machine Learning at Oculus VR (Meta Reality Labs)

Reality Labs (RL) is Meta’s innovation engine for next-generation AR/VR, AI, and wearable technologies. Our Audio team pioneers research and development at the intersection of sound, machine learning, and human experience—enabling new ways for people to connect, communicate, and collaborate.

We are seeking an experienced Research Engineer specializing in Machine Learning Infrastructure to join our Reality Labs Audio team. You will be a technical leader supporting research and product development for AI wearables as part of Meta’s Superhuman Communication & Connection initiative. Your work will focus on building and optimizing ML infrastructure, including data pipelines, model training, evaluation, and validation—leveraging both companywide platforms and developing custom developed project-specific tools.

By joining our team, you will have the opportunity to work on breakthrough technologies that redefine how people connect and communicate, collaborate with world-class researchers and engineers in a fast-paced, mission-driven environment, and shape the future of AI wearables and superhuman audio experiences.

Responsibilities

• Design, implement, and maintain software and hardware pipelines for biosignal and audio data collection, processing, and analysis.
• Collaborate closely with research scientists to translate experimental algorithms and prototypes into robust, scalable engineering solutions.
• Develop tools and infrastructure for data management, annotation, and visualization to accelerate research workflows.
• Work cross-functionally with hardware, software, ML, and UX teams to deliver end-to-end solutions.
• Mentor less experienced engineers and contribute to technical execution.
• Document engineering processes and best practices for knowledge sharing and reproducibility.
• Architect, implement, and maintain scalable data pipelines for biosignal and audio data ingestion, processing, and storage.
• Build and optimize infrastructure for large-scale model training, hyperparameter tuning, and distributed computing.
• Develop robust systems for model evaluation, validation, and deployment, ensuring reproducibility and reliability.
• Integrate companywide ML platforms with project specific tools to accelerate research and product workflows.
• Develop dashboards and visualization tools for monitoring data and model performance.
• Integrate biosignal and audio processing modules into wearable device platforms.
• Optimize system performance for real-time operation, reliability, and scalability.
• Support deployment of machine learning models for biosignal interpretation and audio enhancement.

Minimum Qualifications

• Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta.
• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field.
• 2+ years of experience in machine learning infrastructure, data engineering, or related roles.
• Demonstrated programming skills in Python with a focus on ML training.
• Experience with common ML training platforms (e.g., Pytorch, Tensorflow).
• Proven track record in building scalable data pipelines and ML training/evaluation systems.
• Familiarity with cloud computing, distributed systems, and large-scale data management.
• Demonstrated problem-solving, communication, and collaboration skills.

Preferred Qualifications

• Experience with biosignal and audio data processing for wearable devices.
• Hands-on experience optimizing models for on-device, hardware inference.
• Background in AR/VR, multimodal sensing, or human-computer interaction.
• Experience integrating experimental research code into production infrastructure.
• Knowledge of best practices for ML model validation, monitoring, and deployment.

Key Skills/Competency

• Machine Learning Infrastructure
• Data Pipelines
• Python Programming
• Pytorch/Tensorflow
• Distributed Systems
• Cloud Computing
• Biosignal Processing
• Audio Processing
• Model Deployment
• Scalable Solutions