Member of Technical Staff – Training Data Infrastructure

Member of Technical Staff – Training Data Infrastructure

Requirements

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field.
• 3+ years experience in ML infrastructure development or large-scale data engineering.
• Strong programming skills, particularly in Python and distributed computing frameworks.
• Expertise in building and optimizing high-throughput data pipelines.
• Proven experience with video/image data pre-processing and feature engineering.
• Deep knowledge of machine learning workflows, including model training and data loading systems.
• Track record in performance optimization and system scaling.
• Experience with cluster management and distributed computing.
• Background in MLOps and infrastructure monitoring.
• Demonstrated ability to build reliable, large-scale data processing systems.

Responsibilities

• Build performant pipelines for processing video and multimodal training data at scale.
• Design distributed systems that scale seamlessly with our rapidly growing video and multimodal datasets.
• Create efficient data loading systems optimized for GPU training throughput.
• Implement comprehensive telemetry for video processing and training pipelines.
• Create foundation data processing systems that intelligently cache and reuse expensive computations across the training pipeline.
• Build robust data validation and quality measurement systems for video and multimodal content.
• Design systems for data versioning and reproducing complex multimodal training runs.
• Develop efficient storage and compute patterns for high-dimensional data and learned representations.
• Own and improve end-to-end training pipeline performance.
• Build systems for efficient storage and retrieval of video training data.
• Build frameworks for systematic data and model quality improvement.
• Develop infrastructure supporting fast research iteration cycles.
• Build tools and systems for deep understanding of our training data characteristics.
• Build infrastructure enabling rapid testing of research hypotheses.
• Create systems for incorporating user feedback into training workflows.
• Design measurement frameworks that connect model improvements to user outcomes.
• Enable systematic experimentation with direct user feedback loops.

Preferred Qualifications

No preferred qualifications provided.