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.
What we’re looking for
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 have 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
Additional Information
Base Pay Range
227,332.00 - 312,581.50 USD Annual
This role may also be eligible for Equity Compensation and/or Bonus Incentive Compensation. Procore is committed to offering competitive, fair, and commensurate compensation. Actual compensation will be based on a candidate’s job-related skills, experience, education or training, and location.
For Los Angeles County (unincorporated) Candidates
Procore will consider for employment all qualified applicants, including those with arrest or conviction records, in accordance with the requirements of applicable federal, state, and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act.
A criminal history may have a direct, adverse, and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: 1. appropriately managing, accessing, and handling confidential information including proprietary and trade secret information, as well as accessing Procore's information technology systems and platforms; 2. interacting with and occasionally having unsupervised contact with internal/external customers, stakeholders, and/or colleagues; and 3. exercising sound judgment.
