Machine Learning Engineer

Cantina

. Build and scale systems for ingesting, processing, and delivering large-scale video and multimodal data for model training .

Posted 10/27/2026full-timeSingapore • SingaporeMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and scaling large-scale data systems and pipelines for machine learning, with a focus on dataset curation, filtering, and quality improvement. Proficient in cloud-based data storage and processing, container orchestration, and implementing best practices for data management.

Highest-signal resume keywords

Large-Scale Data SystemsDistributed Data ProcessingContainer OrchestrationCloud-Based Data StorageVLM-Based Captioning

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonDataset CurationData FilteringQuality ImprovementData Pipeline DesignOrchestration ToolsSemantic Data SelectionVideo ProcessingMachine LearningData Analysis

Soft Skills

Problem-SolvingCommunicationDocumentation

Tools & Technologies

KubernetesDockerAWSGCSAzurePySparkRayAirflowFFmpegOpenCV

Industry Keywords

Data Processing FrameworksContainerizationCuration PipelinesQuality/Aesthetic Scoring ModelsEmbedding-Based Filtering

Tech Stack

Tools & technologies

AirflowAWSAzureCloudDockerFFmpegKubernetesPySparkPythonRay

About the role

Key responsibilities & impact

Build and scale systems for ingesting, processing, and delivering large-scale video and multimodal data for model training
Own the full pipeline from raw content to curated, filtered, and training-ready datasets
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 and implement curation pipelines for selecting, filtering, and retaining video and image content for model training
Build and improve VLM-based captioning and metadata generation workflows at scale across 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 for near-deduplication 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 high-quality, training-ready video data across different training regimes
Partner closely with curation and modeling teams to operationalize evolving dataset recipes and improve model outcomes

Requirements

What you’ll need

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

Comp & perks

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