2023 AI Research - Internship

Title: 2023 AI Research - Internship

Company: Samsung Research America (SRA)

Lab:  Artificial Intelligence Center

Location: Mountain View, CA

Lab Summary:

The Artificial Intelligence Center (AIC) at Samsung Research America (SRA), located in Mountain View, CA, is looking for an outstanding and highly self-motivated summer research interns. We are looking for candidates with expertise in deep learning, computer vision and language understanding, with research experience in the following areas: large-scale visual recognition and detection, object tracking, video understanding, deep learning, deep model compression, facial analysis (recognition, expression, and attributes perception), video summarization and NLP. The candidate will work with a team of research scientists and engineers tackling real-world problems involving Samsung’s Artificial intelligence products. The candidate can also have the opportunity to publish at top-tier conferences in deep learning, computer vision and language understanding. As a research engineer, you will be involved in very promising team projects with talented people at Samsung. You will benefit a lot by working in a fun and creative environment. AIC is a key part of Samsung’s global R&D effort and aims to have direct impact on future Samsung products reaching hundreds of millions of users worldwide.   

Position Summary:

Samsung Research America AI center, located in Mountain View, CA, is currently recruiting world-class students who can thrive in a fast-pace, cross team, results-driven environment, with focus on highly visible, challenging, and cross discipline projects.

Common Essential Duties & Responsibilities:

• Develop and implement novel deep learning/reinforcement learning algorithms for natural language processing (text, speech), audio, computer vision and sensor data in various applications (e.g., robotics, AR, etc.)
• Contribute to the research activities of our team
• Generate creative solutions (patents) and publish in top conferences (papers)

Background / Experience:

Experience in one or more of the following areas:

• Strong background in deep learning technologies (e.g, Transformer, CNN, RNN, Sequence to Sequence models, etc.)
• Strong background in machine learning (supervised learning, transfer learning, GAN, one-shot or few shot learning, unsupervised learning, semi-supervised learning, weakly supervised learning, meta-learning, outlier detection, etc.)
• Experience in conversational AI technologies: natural language processing (e.g., language models, semantic parsing, Question Answering, natural language generation etc.), dialogue (e.g., state tracking, policy learning), and representation learning (embedding, conceptualization, etc.)
• Experience in knowledge augmented AI technologies (e.g., language prompt, knowledge graph, neuro-symbolic learning)
• Experience in multimodal AI technologies (e.g., visual+text, audio+visual, audio+sensor, etc.) for various multimodal applications (e.g., TTS, audio separation, healthcare, etc.)
• Experience in on-device AI technologies such as model compression, lightweight model architecture design, efficient model adaptation and continual learning
• Experience in ultra-small memory deep learning and digital signal processing for TinyML on chip/microcontroller
• Experience in runtime memory efficient deep learning for real-time high resolution, high frame rate multimedia data intelligence
• Proficiency in a neural network library (e.g., PyTorch, TensorFlow
• Proven track record of research/publications on machine learning and artificial intelligence field (NeurIPS, ICML, ICLR, AAAI, IJCAI, CVPR, ICCV, ECCV, ACL, EMNLP, NAACL, TACL, etc.)

Necessary Skills / Attributes:

• Team work and communication skills are required
• Ph.D. student/degree in CS, EE, or related field is required

Compensation for this role is expected to be an hourly paid rate between $48 and $70, but may be higher or lower in other states due to geographic differentials in the labor market. Actual pay will be determined considering factors such relevant skills and experience, and comparison to other employees in the role.