Research Scientist

Cantina Labs; Singapore; Presencial; SGD 70.000 - 100.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive salary and generous company equity

Personal time off and paid holidays

Health insurance

Global travel insurance

Monthly spending stipend: $500 (~S$635)

All equipment needed for home office

Descripción de la vacante

Cantina Labs in Singapore is seeking a motivated Research Scientist to lead foundational research on video generation models. This role involves ownership of the full research cycle and requires collaboration with various teams to implement model enhancements. The ideal candidate has strong experience in large-scale data systems, containerization, and generative models, along with a solid research background. Competitive salary and generous equity are part of the offered benefits.

Formación

• Strong experience building large-scale data systems for machine learning.
• Proven skills in distributed data processing and orchestration tools.
• Experience with Docker and Kubernetes for containerization.
• Familiarity with cloud data services and video processing tools.
• Deep knowledge of generative models and their training methods.

Responsabilidades

• Build scalable systems for video data processing for model training.
• Design data pipelines for dataset generation and refreshes.
• Orchestrate workflows and monitor large data processing jobs.
• Implement containerized infrastructure using Kubernetes.
• Optimize cloud data storage for cost and efficiency.
• Develop distillation methods for video generation models.
• Build reward models to align video quality with human judgments.

Conocimientos

Large-scale data systems or pipelines for machine learning workflows

Distributed data processing frameworks (e.g., PySpark, Ray)

Containerization and orchestration (Docker, Kubernetes)

Cloud-based data storage (AWS, GCS, Azure)

Video and media processing tools (FFmpeg, PyAV, DALI, OpenCV)

Multimodal data (video, image, text, audio)

Post-training methods for generative models

Reward modeling and preference-based fine-tuning

Proficiency in Python and machine learning frameworks (PyTorch, JAX)

Independent research and project management

Descripción del empleo

About Cantina

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

Benefits We Offer

• Competitive salary and generous company equity
• Personal time off and paid holidays
• Health insurance
• Global travel insurance: Covers you when traveling internationally
• Monthly spending stipend: $500 (~S$635)
• Equipment: All equipment needed for your home office