Member of Technical Staff - Training Data Infrastructure

Member of Technical Staff – Training Data Infrastructure

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.

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.

No preferred qualifications provided.