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Backflip · 2 months ago

Staff Machine Learning Data Engineer

San Francisco, CA

Full-time

Onsite

Lead/Staff

Backflip is building a foundation model for mechanical design, aiming to unify engineering knowledge into an intelligent design environment. The Staff Machine Learning Data Engineer will lead the development of data pipelines that support this model, collaborating with Machine Learning Engineers to enhance model performance through data-driven experiments.

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Responsibilities

Architect and own Backflip’s ML data pipeline, from ingestion to processing to evaluation

Define data strategy: establish best practices for data augmentation, filtering, and sampling at scale

Design scalable data systems for multimodal training (text, geometry, CAD, and more)

Develop and automate data collection, curation, and validation workflows

Collaborate with MLEs to design and execute experiments that measure and improve model performance

Build tools and metrics for dataset analysis, monitoring, and quality assurance

Contribute to model development through insights grounded in data, shaping what, how, and when we train

Qualification

ML data pipelinesData engineering for MLPythonData augmentation strategiesDistributed systemsLarge-scale data processingAnalytical thinkingData quality focusCollaboration skills

Required

You've built and maintained ML data pipelines at scale, ideally for foundation or generative models, that shipped into production in the real world

You have deep experience with data engineering for ML, including distributed systems, data extraction, transformation, and loading, and large-scale data processing (e.g. PySpark, Beam, Ray, or similar)

You're fluent in Python and experienced with ML frameworks and data formats (Parquet, TFRecord, HuggingFace datasets, etc.)

You've developed data augmentation, sampling, or curation strategies that improved model performance

You think like both an engineer and an experimentalist: curious, analytical, and grounded in evidence

You collaborate well across AI development, infra, and product, and enjoy building the data systems that make great models possible

You care deeply about data quality, reproducibility, and scalability

You're excited to help shape the future of AI for physical design