AI Research Scientist, Multimodal Machine Learning

Key Skills

Aartificial intelligence, C Programming, C++, Computer Vision (CV), Deep Learning, Machine learning techniques, Natural Language Processing (NLP), NeurIPS, Python Programming

Job Description

AI Research Scientist, Multimodal Machine Learning Responsibilities

• Define, collaborate, and execute on research that pushes forward the state of the art in multimodal context/content understanding.
• Work collaboratively with other research scientists to develop novel solutions and models in service of contextualized AI for augmented reality.
• Working on curation of large datasets across many modalities (e.g., vision, audio, eye gaze, IMU, sEMG).
• Leading experimental design, implementation, evaluation, and reporting results to enable new capabilities for multimodal machine learning models.
• Play a significant role in healthy cross-functional collaboration.

Minimum Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• PhD degree or equivalent experience in Computer Science, Mathematics, Engineering or a related field
• Publication record in machine learning, AI, computer science, statistics, applied mathematics, data science, or related technical fields
• 3+ years of experience in one or more of the following areas: Deep Learning, NLP, Multimodal, Computer Vision, Speech
• 3+ years of experience developing end-to-end ML pipelines with a focus on dataset preprocessing, model development and evaluation, software integration, and real-time deployment
• Experience writing software (Python and C/C++) and executing complex machine learning experiments
• Research experience in one or more of these areas: NLP, computer vision, Multimodal, Speech

Preferred Qualifications

• Publication record in AI conferences like NeurIPS, EMNLP, ACL, ICCV, CVPR, ICML, NAACL, ICLR, Interspeech, ICASSP.
• Programming experience in Python and hands-on experience with frameworks such as PyTorch.
• Experience with developing machine learning models at scale from inception to deployment.