Member of Technical Staff, Data & ML Infrastructure for Video Models

Responsibilities

• Build and maintain data pipelines for large video generation models, including data ingestion, parsing, filtering, preprocessing, and dataset curation at scale, using tools such as AWS S3 and DynamoDB.
• Design and run annotation workflows across platforms such as MTurk, Prolific, including task design, quality control, and label validation.
• Train, evaluate, and improve smaller supporting models used for data filtering, quality assessment, preprocessing, or other parts of the ML pipeline.
• Partner closely with research and engineering teams to turn experimental workflows into scalable, repeatable systems that support model training and evaluation.
• Own data quality across the pipeline by identifying bottlenecks, failure modes, and low-quality sources, and continuously improving tooling and processes.
• Build internal tools and automation that make it easier to prepare datasets, launch annotation jobs, monitor outputs, and support model development end to end.
• Drive larger pipeline projects from start to finish, such as new dataset creation efforts or upgrades to labeling and preprocessing infrastructure.
• Work within a Kubernetes-based training infrastructure, ensuring datasets are properly prepared, formatted, and delivered to training clusters.
• Profile and optimize research model inference scripts used in preprocessing steps, ensuring that model-driven filtering and transformation stages run within practical time and cost constraints when applied to large-scale raw data.

Requirements

• 3+ years of experience in machine learning, applied ML, data pipelines, or related engineering roles, ideally working on large-scale multimodal, video, or vision-based systems.
• Strong programming skills in Python and solid experience building reliable data processing and preprocessing pipelines for ML workflows.
• Hands-on experience preparing training data for ML models, including parsing, filtering, dataset curation, quality control, and large-scale data handling using tools such as AWS S3 and DynamoDB.
• Familiarity with annotation and labeling workflows, including task design, vendor or crowd-platform orchestration such as MTurk or Prolific, and methods for ensuring label quality.
• Experience working with Kubernetes for orchestrating distributed workloads, including data preprocessing, pipeline execution, and dataset delivery to training clusters.
• Comfort working across cloud and on-demand compute environments such as AWS and RunPod, with the ability to port and optimize pipelines across infrastructure.
• Familiarity with distributed data processing frameworks and experience designing systems that operate reliably at scale across many nodes or workers.
• Working knowledge of PyTorch and the broader deep learning stack, with the ability to read, debug, and optimize research model inference code for use in production preprocessing pipelines.
• Ability to work cross-functionally with research and engineering teams and translate experimental ideas into robust, scalable systems.
• Bachelor's, Master's, or PhD in Computer Science, Machine Learning, Engineering, Mathematics, or a related technical field; experience in generative video, computer vision, or multimodal ML is strongly preferred.

Nice-to-Haves

• Experience training, evaluating, or fine-tuning smaller ML models used for classification, filtering, ranking, quality assessment, or other supporting tasks in an ML pipeline.

Compensation

• Anticipated annual base salary range: $200,000-$260,000 (U.S.).