About the role
Key responsibilities:
- Design, prototype, and refine deep learning models for text-to-video and image-to-video generation using cutting-edge techniques in generative AI (e.g., diffusion models, transformers, temporal GANs)
- Collaborate with 3rd-party AI vendors to evaluate their capabilities, run quality benchmarks, and drive technical integration where applicable
- Lead the creation of in-house solutions to augment 3rd-party offerings, with a focus on improving video quality, generation time, and content relevance
- Develop robust pipelines for dataset curation, preprocessing, training, and model evaluation, including automated visual, temporal, and semantic quality metrics
- Work closely with product managers, engineering teams, and senior leadership to define use cases, set priorities, and bring production-grade AI systems to life
- Run A/B tests, iterate based on performance feedback, and ensure alignment with product experience goals and creative quality standards
- Stay ahead of the curve in the fast-moving GenAI space; guide strategy, share insights internally, and represent the team in technical forums as needed
Skills and attributes for success:
- Strong understanding of LLMs, with hands-on experience in fine-tuning, prompt engineering, and evaluating foundation models for applied use-cases for multimodal generation
- Proven experience with deep learning architectures for generative tasks (e.g., diffusion models, transformers, GANs)
- Good understanding of computer vision, natural language processing, and multimodal learning
- Experience with training large-scale models, transfer learning, and fine-tuning on custom datasets
- Familiarity with ML evaluation techniques, including perceptual quality metrics and user-in-the-loop testing
- Knowledge of model deployment and performance tuning in production environments
- Hands-on experience building and optimizing text-to-video, image-to-video, or similar generative pipelines
- Hands-on experience with Python, SQL, and TensorFlow/PyTorch for implementing and evaluating algorithms
- Excellent communication skills; able to present complex concepts clearly to technical and non-technical stakeholders
- Experience with experimental design and A/B testing methodologies
Preferred education and experience:Bachelor's in Computer Science, Statistics, Mathematics, or related quantitative field with 4+ years of hands-on experience in applied machine learning or deep learning, with at least 2 years working on generative AI or related video technologies.