{"id":2361340,"url":"https://alion.io/job/global-senior-machine-learning-engineer-2","title":"Senior Machine Learning Engineer","company":{"id":1771858,"name":"Global","domain":"global.com","url":"https://alion.io/company/global-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":100,"open_postings":4,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":17,"computed_at":"2026-10-13T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Holborn, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":95000,"max_usd":175000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":34},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false}],"status":"live","first_seen_at":"2026-10-12T15:54:13Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-13T12:40:20Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Accepting applications until:\n13 November 2026Job Description\nYour New Role: Senior Machine Learning Engineer\nGlobal’s Data team is looking for a Senior Machine Learning Engineer to build, deploy and scale machine learning solutions-turning data science ideas into robust, production-grade products.\nAs a Senior Machine Learning Engineerat Global, you’ll support use cases across DAX, our digital ad exchange-such as the cross-device audience identity graph and real-time targeting algorithms. You’ll join a high-performing, cross-functional DAX squad of data engineers, product specialists and analytics experts, helping build and evolve our cutting-edge ad-serving technology for audio and Outdoor. This is a hybrid role based at our Holborn office in central London.\nKey Responsibilities\nModel Development & Optimisation: Design, build and optimise ML and deep-learning models-including for ad targeting and attribution-with a focus on scalability, performance and accuracy, and prototype and evaluate new approaches.\n\nML Pipelines & Real-Time Inference: Build and maintain robust end-to-end ML pipelines covering training, validation, deployment and monitoring, and develop real-time inference systems with low latency and high throughput.\n\nMonitoring & Reliability: Implement model monitoring, drift detection, alerting and retraining, and optimise models for reliability and cost efficiency in AWS.\n\nCollaboration & Enablement: Partner with data engineers to integrate ML workflows into wider platforms (Spark, Databricks), and share best practice and mentor other technical professionals.\n\nWhat You’ll Love About This Role\nThink Big: Build ML and AI solutions that shape products, improve decision-making and unlock growth.\n\nOwn It: Take ideas from concept to production and see the impact of your work in the real world.\n\nKeep it Simple: Turn complex technical challenges into scalable, practical solutions.\n\nBetter Together: Work with smart, supportive people across data, engineering, analytics and the wider business.\n\nWhat Success Looks Like\nIn your first few months, you’ll have:\nBuilt ML products that deliver measurable value, improving Global’s capabilities in areas such as ad targeting and attribution.\n\nEnsured ML models are reliably deployed, monitored and maintained, with automated, reproducible and scalable pipelines.\n\nBuilt real-time systems that operate efficiently and reliably under production demand.\n\nDeveloped a strong understanding of Global’s data ecosystem, tools and operating model, particularly within DAX.\n\nWhat You’ll Need\nProduction ML experience: You’ve delivered ML and deep-learning projects at high data volume commercially, owning deployment, CI/CD, monitoring and lifecycle management.\n\nStrong Python: Solid Python with PyTorch or similar ML frameworks.\n\nModel evaluation: You diagnose why models underperform across data, features and architecture, and make reasoned trade-offs.\n\nReal-time & distributed ML: A strong grasp of production inference patterns, plus Spark and distributed data processing.\n\nReproducibility & tooling: Reproducible environments (UV/Docker) and MLflow or equivalent, on AWS with Spark, Databricks and Snowflake.\n\nEngineering mindset: A focus on reliability, maintainability and continuous improvement.","description_format":"text","description_chars":3274,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United 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