Staff ML Data Engineer (Datagrid)

Procore Technologies

2d•Hybrid

About The Position

We’re looking for a Staff ML Data Engineer to join Procore’s AI & Frontier Models organization. In this role, you’ll be responsible for designing and building the data systems that power frontier‑scale machine learning research and applied AI products, with a particular focus on spatial intelligence and multimodal data. The primary goal of this role is to ensure that researchers and engineers can reliably discover, curate, transform, and operate on large‑scale datasets that move from experimentation to production. As a Staff ML Data Engineer, you’ll work closely with ML researchers, applied ML engineers, and system architects to turn ambiguous research needs into scalable, production‑ready data pipelines. You’ll remain deeply hands‑on while providing technical leadership in data architecture, quality, and operational excellence. This is an opportunity to shape how Procore builds, evaluates, and deploys frontier models by ensuring the underlying data systems are robust, observable, and designed for iteration. This role reports reports into the Manager, Software Engineering, and is based in our San Francisco office, supporting Procore's Datagrid AI Division. Given the collaborative and fast moving nature of this work, we are seeking candidates who are available to work onsite in a hybrid model at a minimum of 3 days per week. This is an immediate opening!

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
8+ years of experience designing and operating complex data systems in production or research‑adjacent environments.
Strong proficiency in SQL and Python; experience with 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 operating 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, with the ability to explain technical tradeoffs to both research and engineering audiences.

Nice To Haves

Experience with technologies such as: ML & Research Data: Large‑scale dataset curation, annotation workflows, experiment tracking, reproducibility tooling
Data Platforms: Databricks, Spark, lakehouse architectures, cloud data warehouses
Streaming & Pipelines: Kafka, Pub/Sub, event‑driven data architectures
Orchestration & Observability: Airflow, Dagster, data quality and lineage tools
Cloud & Infrastructure: AWS or GCP, containerized data workloads, CI/CD, infrastructure‑as‑code
Performance & Cost: 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 (e.g., documents, images, spatial metadata).
Partner closely with researchers and architects to translate experimental workflows into reliable, repeatable data systems.
Lead the 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.