Machine Learning Engineer

As a Machine Learning Engineer at Cantina Labs, you'll build and scale systems for ingesting, processing, and delivering large-scale video and multimodal data for model training. You own the full pipeline from raw content to curated, filtered, and training-ready datasets, partnering closely with curation and modeling teams to operationalize evolving dataset recipes and improve model outcomes. You'll join the growing Singapore team at a social AI company developing advanced real-time models that power character creation and storytelling platforms.

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 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 and 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

What we offer

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

About Cantina Labs

Cantina Labs is a social AI company developing advanced real-time models for character creation, storytelling, and content creation. The flagship Cantina platform brings characters to life and powers ecosystems for how people tell stories, connect, and create.