{"id":2983426,"url":"https://alion.io/job/cushman-wakefield-principal-machine-learning-engineer","title":"Principal Machine Learning Engineer","company":{"id":448742,"name":"Cushman & Wakefield","domain":"cushmanwakefield.com","url":"https://alion.io/company/cushmanwakefield-2","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":178500,"max":210000,"currency":"USD","period":"year","gross":null,"usd_annual":210000},"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Function Calling","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Azure DevOps","optional":true},{"name":"Feature Store","optional":true},{"name":"GitHub Actions","optional":true},{"name":"LLM","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-10-14T00:00:00Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-26T00:36:06Z","board_verified":true,"closed_at":null,"days_open":12,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":12},"description":"Job Title\nPrincipal Machine Learning EngineerJob Description Summary\nWe are seeking a Principal ML Engineer to build and maintain artificial intelligence and machine learning systems for our business. This role combines deep technical execution with broad influence: tackling the most complex machine learning problems, designing and implementing data architectures/models, MLOps and LLMOps, and monitoring and optimization as key software, while raising the bar for engineering excellence across the Data Organization.Reporting to the Senior Director, Data Science & AI, the Principal ML Engineer is expected to move fluidly across project/product teams - embedding in projects for days or months at a time to unblock, accelerate, and uplift delivery. They will mentor other engineers at every level, push standards for technical excellence, and help establish the ML engineering, and architectural principles that guide our work. The primary platform is Databricks and the Azure cloud ecosystem, with the expectation of evaluating and adopting additional data technologies as the platform evolves.\nJob Description\nRole Summary\nWe are seeking a Principal ML Engineer to build and maintain artificial intelligence and machine learning systems for ourbusiness. This role combines deep technical execution with broad influence: tackling the most complex machine learning problems, designing and implementing data architectures/models, MLOps and LLMOps, and monitoring and optimization as key software, while raising the bar for engineering excellence across the Data Organization.\nReporting to the Senior Director, Data Science & AI, the Principal ML Engineer is expected to move fluidly across project/product teams - embedding in projects for days or months at a time to unblock, accelerate, and uplift delivery. They will mentor other engineers at every level, push standards for technical excellence, and help establish the ML engineering, and architectural principles that guide our work. The primary platform is Databricks and the Azure cloud ecosystem, with the expectation of evaluating and adopting additional data technologies as the platform evolves.\nKey Responsibilities\nAI/ML Model Development\nDemonstrable experience shaping the end-to-end architecture, development, and deployment of AI, including machine learning, GenAI, and Agentic models that are tailored to business use cases.\nDesign and develop agentic AI systems - including multi-agent orchestration, tool-use, evaluation, and autonomous task-execution pipelines - to automate complex enterprise workflows.\nEnforce model development standards encompassing data preprocessing, feature engineering, model selection, hyperparameter tuning, evaluation, and documentation.\nPartner with data engineering teams to ensure robust, scalable, and high-quality data pipelines that support model training and inference.\nAI Operations (MLOps / LLMOps)\nImplement the organization's MLOps and LLMOps capabilities, including CI/CD pipelines for data wrangling, feature engineering, model training, evaluation, deployment, and monitoring.\nHelp define and enforce standards for model versioning, experiment tracking, reproducibility, and model registry management.\nImplement robust model monitoring frameworks to detect performance degradation, data drift, concept drift, and bias in production systems, with automated alerting and retraining triggers.\nAI Governance\nImplement AI governance frameworks, including policies for model risk management, fairness, explainability, privacy, and security.\nChampion sound AI principles across the global business, ensuring that human oversight and accountability are embedded in every stage of the AI development lifecycle.\nEssential Skills, Knowledge & Experience\n10+ years building software or machine learning systems, including 6+ years shipping and operating machine learning in production - served, monitored, and maintained after launch.\nPrior experience as the senior-most or near-senior-most engineer on a production ML or data platform, with responsibility for architectural decisions others had to live with.\nA track record of setting technical standards that outlasted your own involvement, and of leading through influence without formal authority.\nStrong hands-on Python and SQL, and the software engineering fundamentals that keep ML systems maintainable at scale - version control, automated testing, code review, modular design - plus the discipline to enforce them across teams.\nExpert hands-on proficiency with Databricks (Spark, Lakeflow Declarative Pipelines, Delta Lake, Lakebase/Postgres, Unity Catalog, Model Serving) and the Azure data ecosystem. Deep experience on a comparable lakehouse platform will be considered, with the expectation of becoming effective on Databricks quickly.\nExperience with MLflow or an equivalent for experiment tracking, model registry, tracing, and reproducibility.\n At least one year building agentic systems that reached production use, or equivalent demonstrated work. You should be able to walk us through a system you built: how it was orchestrated, how you evaluated it, what guardrails you put around it, how it failed, and what standard you set for whoever built the next one.\nProven ability to mentor engineers at every level and materially raise the technical standard of the teams you work with.\nFamiliarity with modern machine learning frameworks and a strong instinct for reusable, scalable engineering patterns.\nPreferred Skills, Knowledge & Experience\nExperience establishing an ML or AI platform capability in an organisation that did not previously have one.\nExperience embedding across multiple teams, geographies and time zones as a principal-level technical contributor.\nFamiliarity with CI/CD and infrastructure-as-code tooling for data pipelines using Azure DevOps, Databricks Declarative Automation Bundles (DABs), GitHub Actions, Terraform, or equivalent.\nGood understanding of data governance, metadata management, and cataloguing in enterprise environments.\nExposure to model serving and inference at scale - containers, REST endpoints, latency and throughput targets - or to feature stores, retrieval pipelines, or LLM cost-attribution tooling.\nModel Context Protocol (MCP) or comparable tool-integration standards.\nCommercial real estate, professional services, or another domain where the data is messy, entities are inconsistently defined, and subject matter experts are the source of truth.\nCushman & Wakefield also provides eligible employees with an opportunity to enroll in a variety of benefit programs, generally including health, vision, and dental insurance, flexible spending accounts, health savings accounts, retirement savings plans, life, and disability insurance programs, and paid and unpaid time away from work. In addition to a comprehensive benefits package, Cushman and Wakefield provide eligible employees with competitive pay, which may vary depending on eligibility factors such as geographic location, date of hire, total hours worked, job type, business line, and applicability of collective bargaining agreements.\nThe compensation that will be offered to the successful candidate will depend on factors such as whether the position is covered by a collective bargaining agreement, the geographic area in which the work will be performed, market pay rates in that area, and the candidate’s experience and qualifications.\nThe company will not pay less than minimum wage for this role.\nThe compensation for the position is: $ 178,500.00 - $210,000.00Cushman & Wakefield is an Equal Opportunity employer to all protected groups, including protected veterans and individuals with disabilities. Discrimination of any type will not be tolerated.\nIn compliance with the Americans with Disabilities Act Amendments Act (ADAAA), if you have a disability and would like to request an accommodation in order to apply for a position at Cushman & Wakefield, please call the ADA line at 1-888-365-5406 or email . Please refer to the job title and job location when you contact us.\nINCO: “Cushman & Wakefield”","description_format":"text","description_chars":8122,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Dental insurance","Retirement plans"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Commercial Real Estate","Property Management","Real Estate Brokerage","Facilities Management"],"lifecycle":[{"event":"open","at":"2026-10-21T08:02:55Z"}],"visa":[],"liveness":{"score":26,"band":"fade","label":"Fading","p_open":1,"p_active":0.742,"p_room":0.35,"age_days":11,"expected_fill_days":5,"reasons":["conf:2","velocity","win:tail","comp:brand"],"computed_at":"2026-10-25T05:49:26Z"},"pay":{"stated_usd_annual":210000,"is_top_pay":false},"html_url":"https://alion.io/job/cushman-wakefield-principal-machine-learning-engineer","json_url":"https://alion.io/job/cushman-wakefield-principal-machine-learning-engineer.json","meta":{"generated_at":"2026-10-26T00:49:46Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":948,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-27T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":448742},"rest":"https://alion.io/mcp/rest/get_company?id=448742"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fcushman-wakefield-principal-machine-learning-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fcushman-wakefield-principal-machine-learning-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fcushman-wakefield-principal-machine-learning-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/cushman-wakefield-principal-machine-learning-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fcushman-wakefield-principal-machine-learning-engineer"}]}