Machine Learning Engineer – Data Pipelines

About the role

Cantina Labs is building a social AI platform that brings characters to life through real‑time multimodal models. We are expanding our Singapore team and need a Machine Learning Engineer to design, build, and scale the data pipelines that feed our large‑scale video and image datasets into model training.

Key responsibilities

• 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 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 curation pipelines that select, filter, and retain video and image content for model training, including image‑text pair datasets.
• Build and improve VLM‑based captioning and metadata generation workflows at scale.
• Develop quality and aesthetic scoring models, CLIP‑based semantic filtering, and other signal‑extraction approaches for data selection.
• Build tooling for deduplication workflows over large video corpora.
• Analyze dataset composition, identify quality issues, and iterate on curation logic to improve training outcomes.

Required profile

• 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), understanding trade‑offs around cost, throughput, and storage layout.
• Experience with VLM‑based captioning pipelines or similar multimodal processing workflows.

Required skills

• PySpark
• Ray
• Airflow
• Docker
• Kubernetes
• AWS
• Google Cloud Storage (GCS)
• Azure
• Vision‑Language Model (VLM) captioning pipelines
• CLIP‑based semantic filtering