ML Research Engineer

Key Responsibilities

- Lead and execute cutting-edge AI research projects that enhance PYLER’s core capabilities in video classification, contextual similarity, and other video understanding technologies — or propose innovative, data-centric approaches to solving challenges in the video understanding domain

- Collaborate closely with the team lead to identify and drive high-impact AI research initiatives with ownership and accountability

- Design data collection and labeling strategies tailored to each project, optimizing for research efficiency and scalability

- Regularly communicate with fellow ML engineers to exchange constructive feedback and foster a culture of excellence and innovation

- Architect and develop multimodal AI systems that integrate video, audio, and text data for advanced video understanding tasks

- Build systems to monitor, refine, and maintain deployed models in production environments

Requirements

- Master’s degree or higher in a relevant field (e.g., Computer Science, AI, Electrical Engineering, etc.)

- Strong interest and hands-on research experience in Video Understanding and Multimodal AI, particularly involving Vision and NLP

- Proven research expertise and passion in one or more of the following areas:

- Video Understanding

- Video Representation Learning

- Language Modeling

- Multimodal AI

- PhD with at least 1 year of industry experience, or Master’s with 3+ years of relevant industry experience

- Demonstrated leadership and initiative in independently driving research projects from concept to execution

- Highly proactive and responsible, with a strong sense of ownership over assigned work

- Excellent problem-solving skills and communication abilities across interdisciplinary teams

- Proficiency in deep learning frameworks such as PyTorch or TensorFlow

- Solid understanding of AI/ML principles and hands-on experience training and optimizing models

- Willingness to continuously learn and embrace emerging technologies

- Strong team collaboration skills, with a proven ability to deliver results in a team setting

- Familiarity with collaboration tools such as Git, Notion, etc.

Preferred Qualifications

- End-to-end experience in designing, developing, deploying, and maintaining large-scale ML products in production environments

- Hands-on experience with training large models and applying training optimization or acceleration techniques

- Publication record in top-tier AI conferences such as NeurIPS, ICML, ICLR, AAAI, ACL, EMNLP, CVPR, ICCV, ECCV, KDD, or SIGGRAPH, particularly in video, vision + language, or multimodal research

- Strong performance in international competitions (e.g., Kaggle, academic challenges, national AI contests)

- Excellent written and verbal communication skills in English