Machine Learning Engineer (Singapore)

Cantina; Singapore; On-site; SGD 90,000 - 150,000; Full time

Benefits offered by this job

Competitive salary and equity

Personal time off and holidays

Health insurance

Global travel insurance

Monthly stipend: $500 (~S$635)

Home office equipment

Job summary

Cantina Labs is seeking an ML Engineer in Singapore to build and scale data pipelines for large-scale video and multimodal data used in model training. You will own the end-to-end pipeline from raw content to curated, training-ready datasets with emphasis on speed, reproducibility, and cost-efficiency.

You will work with curation and modeling teams to evolve dataset recipes, implement VLM-based captioning and filtering, and optimize data storage, processing, and deduplication at scale.

Qualifications

• Experience designing and scaling large-scale data systems for ML.
• Proficient in distributed data processing (PySpark or Ray).
• Experience with containerization and orchestration (Docker, Kubernetes).
• Familiar with cloud storage/compute across AWS, GCS, or Azure.

Responsibilities

• Design and scale distributed data pipelines for preprocessing, dataset generation, and refreshed datasets.
• 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 systems.
• Optimize cloud storage and movement across providers for cost, throughput, and efficiency.
• Define and implement best practices for dataset storage, versioning, caching, retention, and access patterns.
• Design curation pipelines to select, filter, and retain content for model training.

Skills

Data pipelines

PySpark

Ray

Airflow

Docker

Kubernetes

Python

Video/Multimodal ML

CLIP/embedding filtering

FFmpeg/OpenCV

Education

BS/MS in CS/Math/ML

Tools

Airflow

Docker

Kubernetes

PyTorch

TensorFlow

Job description

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 an ML Engineer to join our growing Singapore team! In this role, you will build and scale systems for ingesting, processing, and delivering large-scale video and multimodal data for model training. You'll own the full pipeline — from raw content to curated, filtered, and training-ready datasets — with a focus on speed, reliability, reproducibility, and cost-efficiency. You'll partner closely with curation and modeling teams to operationalize evolving dataset recipes and iterate on approaches that improve model outcomes.

What You’ll Do:

• 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

• Design and implement curation pipelines that determine which video and image content is selected, filtered, and retained for model training, including image-text pair datasets used in joint training regimes

• Build and improve VLM-based captioning and metadata generation workflows at scale across both video and image data

• Develop and apply quality and aesthetic scoring models, CLIP-based semantic filtering, and other signal-extraction approaches for data selection

• Build tooling to support deduplication workflows at scale, including near-dedup and exact deduplication pipelines over large video corpora

• Analyze dataset composition, identify quality issues, and iterate on curation logic to improve training outcomes

• Define and evolve standards for what constitutes high-quality, training-ready video data across different training regimes

What You’ll Bring:

• Strong hands-on experience building or scaling large-scale data systems and pipelines for machine learning, including dataset curation, filtering, and quality improvement

• 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

• Experience with VLM-based captioning pipelines or quality/aesthetic scoring models for video or image data, including curation of image-text pair datasets for joint image-video training

• Familiarity with CLIP-based or embedding-based filtering and semantic data selection techniques

• Familiarity with video and media processing tools such as FFmpeg, PyAV, DALI, or OpenCV, and relevant libraries such as Decord, torchvision, PyTorchVideo, or torchaudio

• Proficiency in Python

• Strong problem-solving, communication, and documentation skills

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