{"id":115307,"url":"https://alion.io/job/vanguard-machine-learning-engineer-specialist-2","title":"Machine Learning Engineer, Specialist","company":{"id":253,"name":"The Vanguard Group","domain":"vanguard.com","url":"https://alion.io/company/vanguard","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":89,"ghost_share":0.079,"stale_share":0.506,"repost_share":0.202,"time_to_fill_p50_days":27,"computed_at":"2026-10-30T05:48:23Z"}},"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":["Malvern, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":128000,"max_usd":246000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1332},"experience_years_min":5,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"Azure","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Feature Store","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-06-02T00:00:00Z","employer_posted_date":"2026-09-30","last_verified_at":"2026-10-31T00:56:44Z","board_verified":true,"closed_at":null,"days_open":151,"trust":{"level":"stale","repost_count":1,"flags":["stale"],"days_open":149},"description":"Supports and performs the development and programming of machine learning integrated software algorithms to structure, analyze, and leverage data in a production environment.\nCore Responsibilities\nLeverages data pipeline designs and supports the development of data pipelines to support model development. Proficient with software tools that develop data pipelines in a distributed computing environment (PySprak, GlueETL).\n\nSupports integration of model pipelines in a production environment. Develops understanding of SDLC for model production.\n\nReviews pipeline designs, makes data model design changes as needed. Documents and reviews design changes with data science teams.\n\nSupports data discovery & automated ingestion for model development. Performs detailed analysis of raw data sources for data quality, applies business context, and model development needs.\n\nEngages with internal stakeholders to understand and probe business processes in order to develop hypotheses. Brings structure to requests and translates requirements into an analytic approach. Participates in and influences ongoing business planning and departmental prioritization activities.\n\nRuns model monitoring scripts, follows process for alerts to management as needed. Addresses issues found in data pipelines from model monitoring alerts.\n\nParticipates in special projects and performs other duties as assigned.\n\nQualifications\nUndergraduate degree or equivalent experience; a graduate degree is preferred.\n\nMinimum of 5 years of relevant work experience.\n\nAt least 3 years of hands-on experience designing ETL pipelines using AWS services (e.g., Glue, SageMaker).\n\nProficiency in programming languages, particularly Python (including PySpark, PySQL) and familiarity with machine learning libraries and frameworks.\n\nStrong understanding of cloud technologies, including AWS and Azure, and experience with NoSQL databases.\n\nFamiliarity with Feature Store usage, LLMs, GenAI, RAG, Prompt Engineering, and Model Evaluation.\n\nExperience with API design and development is a plus.\n\nSolid understanding of software engineering principles, including design patterns, testing, security, and version control.\n\nKnowledge of Machine Learning Development Lifecycle (MDLC) best practices and protocols.\n\nUnderstanding of solution architecture for building end-to-end machine learning data pipelines.\n\nSpecial Factors\nSponsorship\nVanguard is not offering visa sponsorship for this position.About Vanguard\nAt Vanguard, we don't just have a mission-we're on a mission.\nTo work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.\nHow We Work\nVanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. 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