Machine Learning Engineer

Placed at the heart of Gensler’s People + Process + Technology, the Global Design Technology Studio is advancing a growing data and machine learning foundation that is shaping how the firm designs and delivers its work for generations to come. We believe that when the right intelligence is in the hands of the right people at the right moment, we can transform Gensler and our industry so that our designers can make better decisions, unlock new value for clients, and create built environments that are more responsive, more meaningful, and more impactful than ever before.

In the role of Machine Learning Engineer, you will drive and operationalize machine learning across AEC data domains including BIM, geospatial, design-performance, operational, and other project and practice data, translating technical capability into tools and workflows that practitioners can use. This is a digitally transformative, hands‑on engineering role focused on building, deploying, maintaining, and optimizing machine learning systems and data pipelines, working with large‑scale datasets to power production‑ready intelligent systems and drive scalable outcomes across the firm.

Success in this role means delivering reliable, secure, and well‑governed ML systems that integrate into design workflows, are adopted by teams, and improve decision‑making across the firm. You will help shape these capabilities inside an established Design Technology team, working alongside AI and data engineers, data scientists, designers, and product stakeholders to translate ambitious, pioneering ideas into production‑ready platforms.

• Design, build, and maintain reliable Azure-based ETL/ELT pipelines that deliver clean, documented data to models, APIs, internal applications, dashboards, and Gensler IP.
• Own the production ML lifecycle on Azure — deployment, monitoring, versioning, retraining, rollback, and incident response — including model registries, feature stores, evaluation frameworks, and benchmarking.
• Implement CI/CD, testing, reproducibility, and deployment standards for model and data workflows.
• Extend and mature cloud data architecture and modeling strategies that support analytics and machine learning workloads.
• Source, profile, and document datasets with business owners to confirm provenance, quality, fitness for use, and alignment with client-data and governance requirements.
• Evaluate model performance and address root causes across data quality, feature engineering, training methodology, and architecture.
• Translate research prototypes and experimental models into documented, production‑ready systems.
• Work across AEC data types such as BIM/Revit/IFC, geospatial, design‑performance, occupancy, and other built‑environment datasets.
• Integrate machine learning outputs into design and delivery workflows so practitioners can access insights earlier and make more informed project decisions.
• Apply responsible AI and data governance practices, including transparency, traceability, human oversight, and appropriate handling of client and project data.
• Support the firm’s data‑driven design community through technical guidance, code review, and knowledge sharing.
• Help define and track success criteria for deployed systems, including reliability, adoption, reuse, and measurable workflow or decision impact.

• Bachelor's or advanced degree in Computer Science, Software Engineering, Data Science, Statistics, Applied Mathematics, or a related field; equivalent production ML or data engineering experience considered.
• 8+ years of hands‑on experience building, deploying, and operating production ML systems on Azure, with practical MLOps and CI/CD experience including experiment tracking, deployment hygiene, monitoring,…