Staff ML Data Engineer (Datagrid)
About The Position
Procore is seeking a Staff ML Data Engineer to join its AI & Frontier Models organization and support the Datagrid AI Division. The role designs and builds data systems that power frontier-scale machine learning research and applied AI products, with a particular focus on spatial intelligence and multimodal data. Its goal is to enable researchers and engineers to reliably discover, curate, transform, and operate on large-scale datasets as they move from experimentation into production. The Staff ML Data Engineer works closely with ML researchers, applied ML engineers, and system architects to turn ambiguous research needs into scalable, production-ready data pipelines. The position remains hands-on while providing technical leadership in data architecture, quality, and operational excellence, helping ensure data systems are robust, observable, and designed for iteration. The role reports to the Manager, Software Engineering and is based in Procore’s San Francisco office. It follows a hybrid onsite model requiring at least three days per week in the office. The position is described as an immediate opening.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- At least 8 years of experience designing and operating complex data systems in production or research-adjacent environments.
- Strong proficiency in SQL and Python, with experience in data-intensive or distributed systems.
- Proven experience building scalable data pipelines that support machine learning training, evaluation, or inference workflows.
- Solid understanding of data modeling, dataset lifecycle management, and data quality best practices.
- Comfort working in highly ambiguous problem spaces and collaborating closely with researchers and architects.
- Demonstrated ability to lead through direct technical contribution, mentorship, and setting engineering standards.
- Strong communication skills, including the ability to explain technical tradeoffs to research and engineering audiences.
Nice To Haves
- Experience with large-scale dataset curation, annotation workflows, experiment tracking, or reproducibility tooling.
- Experience with Databricks, Spark, lakehouse architectures, or cloud data warehouses.
- Experience with Kafka, Pub/Sub, or event-driven data architectures.
- Experience with Airflow, Dagster, or data quality and lineage tools.
- Experience with AWS or GCP, containerized data workloads, CI/CD, or infrastructure as code.
- Experience optimizing data pipelines for GPU-backed training and large-scale inference workloads.
Responsibilities
- Act as the technical lead for data engineering efforts supporting frontier model research and applied ML systems.
- Design, build, and maintain scalable batch and streaming pipelines for multimodal data, including documents, images, and spatial metadata.
- Partner with researchers and architects to translate experimental workflows into reliable, repeatable data systems.
- Lead development of dataset curation, versioning, and lineage workflows that support rapid experimentation and reproducibility.
- Establish and uphold standards for data quality, validation, observability, and cost efficiency across AI data pipelines.
- Contribute to data architecture decisions spanning research environments and production systems.
- Identify gaps or inefficiencies in existing data workflows and run proofs of concept to evaluate improvements.
- Mentor other engineers through code reviews, design discussions, and hands-on collaboration.
Benefits
- The role may be eligible for equity compensation and/or bonus incentive compensation.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
