Staff Applied Research Scientist (Datagrid)

Procore Technologies

1d•Hybrid

About The Position

We’re looking for a Staff Applied Research Scientist to join Procore’s AI & Frontier Models organization. In this role, you’ll act as the hands‑on technical leader for applied machine learning systems that extract spatial intelligence from construction drawings, BIM, and project data. The primary goal of this role is to design and deliver reliable, scalable ML systems that reduce design risk, improve constructability, and expand the range of spatial problems Procore teams can solve. As a Staff Applied Research Scientist, you’ll partner with ML engineers, software engineers, product managers, and construction domain experts to lead day‑to‑day technical execution for spatial intelligence initiatives. Use your expertise in applied machine learning, software architecture, and system design to translate complex, ambiguous problems into high‑quality production systems. This is an opportunity to remain deeply hands‑on while shaping technical direction and raising the engineering bar for the team—join us and help define how spatial intelligence shows up in real construction workflows. 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, Master’s, or PhD in Computer Science, Engineering, Machine Learning, or a related field, or equivalent practical experience.
8+ years of professional experience building production software systems, including applied machine learning components.
Strong experience designing, training, and deploying ML models using Python and modern ML frameworks.
Solid foundation in computer science fundamentals, including data structures, algorithms, and system design.
Experience working with complex or high‑dimensional data such as images, documents, or structured technical datasets.
Demonstrated ability to lead technically through direct contribution, mentorship, and architectural decision‑making.
Strong system‑level thinking, with an understanding of reliability, scalability, cost, and operational constraints.
Clear communication skills and the ability to explain technical decisions and tradeoffs to cross‑functional stakeholders.

Nice To Haves

ML & Data: PyTorch, TensorFlow, NumPy, Pandas, HuggingFace, self‑supervised or multimodal learning workflows
Computer Vision & Spatial Data: OpenCV, document understanding pipelines, geometric or graph‑based representations, 2D/3D spatial reasoning
Data & Training Infrastructure: Distributed training, experiment tracking, dataset versioning, large‑scale annotation workflows
Backend & Systems: Python‑based services, REST or gRPC APIs, batch and streaming data pipelines
Cloud & DevOps: Containerized ML services, Kubernetes, cloud compute and storage (AWS, GCP, or equivalent)
Quality & Operations: Model evaluation frameworks, monitoring and alerting, performance and cost optimization in production

Responsibilities

Act as the day‑to‑day technical lead for applied ML projects within the Frontier Models & Spatial Intelligence team.
Design, implement, and iterate on machine learning systems that analyze 2D drawings and BIM data to detect clashes, inconsistencies, and constructability risks.
Lead hands‑on development of model training, evaluation, and inference pipelines in close collaboration with other engineers.
Drive proof‑of‑concept and exploratory work to reduce ambiguity and rapidly validate technical approaches.
Ensure the long‑term health, performance, and maintainability of the team’s ML codebases and supporting systems.
Set and uphold engineering quality standards through code reviews, mentorship, and technical guidance.
Collaborate with partner teams to ensure spatial intelligence systems integrate cleanly into Procore’s broader platform and workflows.
Proactively identify technical risks, architectural gaps, or operational concerns and address or escalate them appropriately.