Requirements

Expected technologies

PyTorch

TensorFlow

Linux

Docker

Operating system

Linux

Our requirements

• Extensive experience in training and optimizing large-scale AI models.
• Strong mathematical background in linear algebra and statistics.
• Publication record in top-tier AI conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, Interspeech, ICASSP).
• Proficiency in Linux programming environments, including Docker.
• Demonstrated expertise in technical programming and deep learning frameworks (e.g., PyTorch, TensorFlow).
• Excellent scientific writing skills, with a track record of publications in reputable venues.
• Outstanding communication abilities and proven capacity to collaborate effectively in diverse, interdisciplinary teams.
• Familiarity with technology development processes and design thinking methodologies.

Optional

• Proven experience in audio signal processing, including microphone array design and evaluation, as well as ML-based processing.
• Knowledge of acoustics, including measurements of audio/acoustic systems.
• Practical knowledge of libraries related to waveform data processing.
• Expertise in one or more of the following areas: Numerical Linear Algebra, Numerical Optimization Techniques, Reinforcement Learning, Spatio-temporal Grounding, Adaptive and Stochastic Control Theory, Signal Processing, Explainable AI (XAI), Dynamical Systems, Cryptography, and AI Security.
• Experience with grant and patent writing.
• Professional experience in porting neural network models on embedded platforms (e.g., ARM).
• Professional experience in developing product-grade code, including high-level software development practices, integration into CI environments, proper testing, packaging, and releasing of software.

Your responsibilities

• Design and implement machine learning algorithms (including deep neural networks) for analyzing audio and video content.
• Conduct scientific literature reviews, contribute to scientific publications, and participate in patent applications.
• Collaborate closely with product management to define product strategy and roadmap.
• Lead software system architecture design to deliver efficient, maintainable code for model training, evaluation, and deployment.
• Design solutions for incorporating machine/deep learning into product features and effectively communicate these solutions to software engineers and business leaders.
• Actively monitor and contribute to the latest developments and trends in AI research, integrating state-of-the-art advancements into research practices.
• Mentor junior researchers and foster intellectual growth within the team.