{"id":2361339,"url":"https://alion.io/job/global-senior-machine-learning-engineer","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":"on_site","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":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Kubernetes","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":"2026-10-12T20:43:55Z","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 central Data Science function is recruiting a Senior Machine Learning Engineer to work across the full breadth of our data and product portfolio.\nAs a Senior Machine Learning Engineerat Global, you’ll be the engineering bridge between data science and production-taking models built by our data scientists and making them robust, scalable and maintainable across audience targeting, advertising measurement and content intelligence. It’s based in central London (Holborn, with occasional travel to Leicester Square).\nKey Responsibilities\nModel Development, Productionisation & Migration (65%): Translate experimental models into reliable, testable production systems; build APIs and serving infrastructure across batch and real-time; design consistent feature pipelines; and audit and migrate existing models to modern standards without disrupting live products.\n\nStandards & Enablement (20%): Define engineering standards for how models are built, tested and deployed, aligned to the MLOps platform, and create reusable templates and documentation that help data scientists work independently.\n\nCross-functional Partnership (15%): Work with data scientists, MLOps, data engineering and product to shape new products early and ensure models are handed off in a deployable, maintainable form.\n\nWhat You’ll Love About This Role\nThink Big: This is a true AI and data-driven space-the models you ship influence what millions of listeners hear and how brands invest their media budgets.\n\nOwn It: You’ll shape how we build going forward, not just maintain what exists-your engineering standards become the team’s standards.\n\nKeep it Simple: You’ll build reusable patterns and templates rather than one-off solutions.\n\nBetter Together: You’ll work across the full range of Global’s data products, partnering with Data Science, MLOps, Data Engineering and Product.\n\nWhat Success Looks Like\nIn your first few months, you’ll have:\nBuilt a clear understanding of Global’s engineering and data science tools, how they connect, and where ML sits commercially.\n\nEstablished strong working relationships and operating models with Data Science and MLOps.\n\nCompleted an audit of existing ML models in production, assessing stability, maintainability and risk.\n\nEngineered a model to a more robust, documented state and built reusable components others can use.\n\nWhat You’ll Need\nProduction ML experience: You’ve delivered ML and deep-learning projects at high data volume in commercial environments, 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 ML & reproducibility: A strong grasp of production inference patterns and reproducible environments (Docker, MLflow or equivalent).\n\nCloud & tooling: AWS, plus SageMaker, Snowflake, Spark/Databricks and Kubernetes.\n\nEngineering mindset: A focus on reliability, maintainability and continuous 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