Research Scientist (Singapore)

Cantina Labs is a social AI company, developing a suite of advanced real-time models that push the boundaries of expression, personality, and realism. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning.

About the Role:

Cantina is expanding, and we're looking for a Research Scientist to join our growing Singapore team! In this role, you will drive foundational research on video generation models, taking ownership across the full research cycle and driving post-training research. Furthermore, you'll collaborate closely with data, infrastructure, and adjacent modeling teams to translate research findings into durable model improvements.

What You’ll Do:

Build and maintain scalable systems for ingesting, preprocessing, and delivering large-scale video data for model training
Design and scale distributed data pipelines for preprocessing, dataset generation, and repeated dataset refreshes
Own workflow orchestration, job scheduling, monitoring, and failure recovery for large-scale data processing jobs
Implement and maintain containerized pipeline infrastructure using Kubernetes or equivalent orchestration systems
Optimize cloud-based data storage and movement across providers (AWS, GCS, or Azure) for cost, throughput, and operational efficiency
Define and implement best practices for dataset storage layout, versioning, caching, retention, and access patterns
Build tooling to support deduplication workflows at scale, including near-dedup pipelines over large video corpora
Research and develop distillation methods for large-scale diffusion and flow-based video generation models, including guidance distillation and adversarial distillation, with a focus on preserving or improving generation quality while reducing inference cost
Develop reward models and preference-based fine-tuning pipelines that align video generation quality with human judgments across dimensions such as aesthetics, motion quality, and prompt adherence
Analyze the relationship between base model behavior and post-training outcomes, and work with the foundation model team to inform pretraining decisions accordingly

What You’ll Bring:

Strong hands-on experience building or scaling large-scale data systems or pipelines for machine learning workflows
Experience with distributed data processing frameworks such as PySpark or Ray, and orchestration tools such as Airflow or equivalent
Familiarity with containerization and container orchestration, including Docker and Kubernetes
Experience working with cloud-based data storage and compute (AWS, GCS, and/or Azure), including tradeoffs around cost, throughput, storage layout, and access patterns
Familiarity with video and media processing tools such as FFmpeg, PyAV, DALI, or OpenCV
Familiarity with multimodal or media data, including video, image, text, and audio
Strong research background in post-training methods for large-scale diffusion or flow-based generative models, with deep hands-on experience in distillation across both inference efficiency and quality preservation
Experience with reward modeling or preference-based fine-tuning for generative models, including RLHF, DPO or equivalent alignment approaches
Solid understanding of the interplay between pretraining and post-training, and how base model properties affect distillation and fine-tuning outcomes
Proficiency in Python and modern machine learning frameworks, with a strong preference for PyTorch or JAX
Track record of independent research, with the ability to drive projects from initial idea through experimental validation
Publications at top-tier venues (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV) preferred
Good understanding of the practical challenges involved in building reliable, scalable, and reproducible data workflows for machine learning systems