Principal Machine Learning Engineer

🔥 5 minutes ago

🇺🇸 United States – Remote

💵 $178.5k - $210k / year

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

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Cushman & Wakefield

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1917

💼 Consulting

🏥 Healthcare

🏗️ Construction

Consulting • Healthcare • Construction

Cushman & Wakefield is a global leader in commercial real estate services, providing a wide range of services including agency leasing, asset services, capital markets, global occupier services, project and development services, tenant representation, and valuation and advisory for various industries such as logistics and industrial, multifamily, retail, life sciences, data centers, and healthcare. The company is committed to driving forward for clients, colleagues, and communities with a focus on diversity, equity, and inclusion, as well as environmental, social, and governance principles. Operating in over 60 countries with 52,000 professionals, Cushman & Wakefield consistently delivers insights and research, such as market reports and sustainability efforts.

📋 Description

• Build and maintain artificial intelligence and machine learning systems for the business • Tackle complex machine learning problems • Design and implement data architectures and models • Develop MLOps and LLMOps capabilities • Monitor and optimize machine learning systems • Move across project and product teams to unblock, accelerate, and improve delivery • Mentor engineers at every level • Establish ML engineering and architectural principles • Shape end-to-end architecture, development, and deployment of AI, machine learning, GenAI, and agentic models • Design and develop agentic AI systems with multi-agent orchestration, tool use, evaluation, and autonomous task-execution pipelines • Enforce standards for data preprocessing, feature engineering, model selection, hyperparameter tuning, evaluation, and documentation • Partner with data engineering teams on robust, scalable data pipelines for model training and inference • Implement CI/CD pipelines for data wrangling, feature engineering, model training, evaluation, deployment, and monitoring • Define and enforce standards for model versioning, experiment tracking, reproducibility, and model registry management • Implement monitoring for performance degradation, data drift, concept drift, and production bias, including automated alerting and retraining triggers • Implement AI governance frameworks covering model risk management, fairness, explainability, privacy, and security • Champion AI principles, human oversight, and accountability across the AI development lifecycle • Report to the Senior Director, Data Science & AI

🎯 Requirements

• 10+ years building software or machine learning systems, including 6+ years shipping and operating machine learning in production • Prior experience as the senior-most or near-senior-most engineer on a production ML or data platform, with responsibility for architectural decisions • Track record of setting technical standards and leading through influence without formal authority • Strong hands-on Python and SQL • Software engineering fundamentals including version control, automated testing, code review, and modular design • Expert hands-on proficiency with Databricks, including Spark, Lakeflow Declarative Pipelines, Delta Lake, Lakebase/Postgres, Unity Catalog, and Model Serving • Experience with the Azure data ecosystem • Experience with MLflow or an equivalent for experiment tracking, model registry, tracing, and reproducibility • At least one year building agentic systems that reached production use, or equivalent demonstrated work • Proven ability to mentor engineers at every level and raise technical standards • Familiarity with modern machine learning frameworks and reusable, scalable engineering patterns • Preferred: experience establishing an ML or AI platform capability • Preferred: experience embedding across multiple teams, geographies, and time zones • Preferred: familiarity with CI/CD and infrastructure-as-code tooling such as Azure DevOps, Databricks Declarative Automation Bundles, GitHub Actions, Terraform, or equivalent • Preferred: understanding of data governance, metadata management, and cataloguing • Preferred: exposure to model serving and inference at scale, feature stores, retrieval pipelines, or LLM cost-attribution tooling • Preferred: Model Context Protocol or comparable tool-integration standards • Preferred: commercial real estate, professional services, or a similarly data-complex domain

🏖️ Benefits

• Health insurance • Vision insurance • Dental insurance • Flexible spending accounts • Health savings accounts • Retirement savings plans • Life insurance • Disability insurance programs • Paid time away from work • Unpaid time away from work