Principal Applied Research Scientist (Datagrid)

Procore Technologies•US - California - Bay Area, CA

1d•$253,552 - $348,634•Hybrid

About The Position

We’re looking for a Principal Applied Research Scientist to join Procore’s AI & Frontier Models organization. In this role, you’ll lead applied research and system development at the intersection of machine learning, spatial reasoning, and construction data to solve some of the industry’s most complex and ambiguous problems. The primary goal of this role is to build defensible spatial intelligence capabilities that extract value from drawings, BIM, and site data today while laying the foundation for 3D understanding, autonomy, and robotics tomorrow. As a Principal Applied Research Scientist, you’ll partner with ML engineers, software engineers, product leaders, and construction domain experts to design, prototype, and deploy frontier ML systems that translate research into measurable business outcomes. Use your expertise in applied machine learning, large‑scale data systems, and technical leadership to drive step‑change improvements in constructability, risk reduction, and operational efficiency. This is a rare opportunity to shape Procore’s long‑term technical direction in spatial intelligence while delivering near‑term ROI—join us and help define the future operating system of construction. 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

PhD or Master’s degree in Computer Science, Machine Learning, Robotics, Computer Vision, or a related field, or equivalent depth of practical experience.
12+ years of experience (or equivalent depth) building and leading complex machine learning or software systems in production and/or applied research environments.
Demonstrated ability to set technical direction and independently lead high‑impact initiatives across multiple teams or a significant business concern.
Deep expertise in applied machine learning, with a track record of translating research ideas into scalable, reliable systems that deliver measurable outcomes.
Strong foundation in computer science fundamentals, including algorithms, data structures, distributed systems, and system architecture.
Proven ability to operate effectively in high‑ambiguity problem spaces, using data, experimentation, and proof‑of‑concept work to create clarity and alignment.
Experience mentoring senior engineers and researchers, and serving as a technical authority and escalation point across an organization.
Excellent communication skills, with the ability to represent technical strategy, tradeoffs, and outcomes to senior leadership and cross‑functional stakeholders.

Nice To Haves

Experience with technologies such as: ML & Model Development: PyTorch, TensorFlow, large‑scale pre‑training or fine‑tuning workflows, multimodal or foundation models
Spatial Intelligence & Perception: Computer vision, document and diagram understanding, geometric or graph‑based reasoning, 2D/3D representations
Data & Research Infrastructure: Large‑scale dataset creation and curation, annotation pipelines, distributed training, experiment management
Systems & Architecture: Designing and evolving ML‑powered platforms and services, cross‑domain integrations, reliability and performance at scale
Cloud & Compute: GPU/accelerator‑based training and inference, containerized systems, Kubernetes, cloud platforms (AWS, GCP, or equivalent)
Evaluation & Operations: Defining evaluation frameworks, model validation strategies, monitoring, and cost‑aware optimization in production

Responsibilities

Lead the technical vision, design, and execution of applied research initiatives focused on spatial intelligence across 2D drawings, BIM, and site data.
Design, train, and evaluate machine learning models for constructability analysis, clash detection, and design risk identification.
Build high‑quality, proprietary annotated datasets and define data strategies that create long‑term competitive advantage.
Translate ambiguous research problems into concrete system designs, proofs of concept, and production‑ready ML pipelines.
Partner closely with engineering and product teams to integrate research outputs into scalable, reliable systems.
Act as a technical SME for spatial intelligence, mentoring senior engineers and researchers across the organization.
Author and review technical documentation, white papers, and design proposals that shape Procore’s AI strategy.
Proactively identify gaps in technical strategy and architecture, and independently drive solutions that improve system quality, robustness, and impact.