ML and CV for Audio

The Audio team within RL Research is looking for a Researcher Engineer with experience in efficient multimodal machine learning to join our team. You will be building technologies that improve the listener's hearing and conversational experiences under challenging listening conditions using wearable computing. We are looking for expertise in building efficient representation models using audio, visual and speech signals; bridge and integrate AI models with conversational context and domain understanding. You will operate at the intersection of large-scale machine learning systems design and development, and egocentric audio-visual learning and computer vision, and partnering with experts in systems processing; and hardware/software co-design.

• Work with AI researchers and audio/acoustics domain experts on designing and building novel low-compute, low-power ML and CV for egocentric audio-visual learning
• Design and build efficient AI engineering frameworks supporting large-scale benchmarking of low-compute, low-power ML and CV models.
• Implement large-scale benchmarking, online and offline evaluation mechanisms, and related hyper-parameter optimizations for efficient audio-visual learning models
• 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 and CV models into research & development platforms for wearables.
• Contribute as relevant to datasets designs and large-scale data processing for real-time evaluations of efficient audio-visual machine learning methods.
• Contribute to technical directions on novel ML research supporting source tracking, source localization, source diarization, and relevant semantic scene understanding with application into egocentric wearable computing in AR and VR.

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• Masters degrees or equivalent experience in Computer Sciences, Computer Engineering, Deep Learning, Artificial Intelligence, Machine Learning, Robotics, Computer Vision, Computational Neuroscience, Signal Processing, Speech and Language technologies, or a related field, or equivalent practical experience.
• Bachelor's degree in computer science, computer engineering, or relevant technical field.
• 2+ years of research experience working on applied computer vision methods.
• 1+ years of research experience working on efficient multimodal machine learning algorithms for low-compute and low-power devices.
• 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 in Python or C++
• Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.
• Experience of building collaborative relationships that lead to impact

• PhD in Computer Sciences, Computer Engineering, Deep Learning, Artificial Intelligence, Machine Learning, Robotics, Computer Vision, Computational Neuroscience, Signal Processing, Speech and Language technologies, or a related field, or equivalent practical experience.
• 3+ yrs of experience working on efficient machine learning or computer vision algorithms.
• Experience with end-to-end real-time ML pipelines, large-scale ML benchmarking, real-time statistical modeling including heuristics-driven computer vision methods.
• Experience working on evaluation and benchmarking for vision based LLamas, or related generative AI models
• Experience working with datasets on preprocessing methods, dataloaders, data tooling and related software engineering platforms.
• Experience with large-scale or distributed cluster computing for training, development and offline inference of machine learning models.
• Experience working and communicating cross functionally in a team environment.
• Experience solving complex problems and comparing alternative solutions, tradeoffs, and diverse points of view to determine a path forward.
• Demonstrated research and software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub).
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops, journals or conferences such as ACL, NeurIPS, ICLR, EMNLP, CVPR, ICCV, ICML, ECCV, ICASSP, InterSpeech, or similar.