Machine Learning Engineer

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 refreshesOwn workflow orchestration, job scheduling, monitoring, and failure recovery for large-scale data processing jobsImplement and maintain containerized pipeline infrastructure using Kubernetes or equivalent orchestration systemsOptimize cloud-based data storage and movement across providers (AWS, GCS, or Azure) for cost, throughput, and operational efficiencyDefine and implement best practices for dataset storage layout, versioning, caching, retention, and access patternsDesign 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 regimesBuild and improve VLM-based captioning and metadata generation workflows at scale across both video and image dataDevelop and apply quality and aesthetic scoring models, CLIP-based semantic filtering, and other signal-extraction approaches for data selectionBuild tooling to support deduplication workflows at scale, including near-dedup and exact deduplication pipelines over large video corporaAnalyze dataset composition, identify quality issues, and iterate on curation logic to improve training outcomesDefine and evolve standards for what constitutes high-quality, training-ready video data across different training regimesWhat 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 improvementExperience with distributed data processing frameworks such as PySpark or Ray, and orchestration tools such as Airflow or equivalentFamiliarity with containerization and container orchestration, including Docker and KubernetesExperience working with cloud-based data storage and compute (AWS, GCS, and/or Azure), including tradeoffs around cost, throughput, storage layout, and access patternsExperience 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 trainingFamiliarity with CLIP-based or embedding-based filtering and semantic data selection techniquesFamiliarity with video and media processing tools such as FFmpeg, PyAV, DALI, or OpenCV, and relevant libraries such as Decord, torchvision, PyTorchVideo, or torchaudioProficiency in PythonStrong problem-solving, communication, and documentation skills