{"id":3684898,"url":"https://alion.io/job/true-talent-solutions-partners-senior-data-machine-learning-engineer","title":"Senior Data & Machine Learning Engineer","company":{"id":4415660,"name":"True Talent Solutions Partners","domain":"truetalentsolutionspartners.com","url":"https://alion.io/company/true-talent-solutions-partners","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"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":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":200000,"max":300000,"currency":"USD","period":"year","gross":null,"usd_annual":300000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"dbt","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FAISS","optional":false},{"name":"Feature Store","optional":false},{"name":"Milvus","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"PostgreSQL","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-11-03T14:21:25Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-08T21:19:27Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"About the Role\nOwn the recommendation and matching stack end to end at an early-stage AI company in the advertising and consumer modeling space. You will build the models, real-time serving systems, and data platform that power personalized ad selection.\nWhat You'll Do\nBuild retrieval, ranking, and re-ranking systems for matching ads with recipients.\n\nDevelop embedding and vector-search infrastructure for real-time ad selection.\n\nOptimize ranking across click-through rate, advertiser return, publisher revenue, and subscriber relevance.\n\nOwn the feature store and serving path, including offline training, online inference, and pipeline reliability.\n\nApply language models and foundation models to subscriber tagging, content understanding, and user modeling.\n\nLead projects from prototype to production, including online learning, feedback loops, ETL/ELT pipelines, and warehouse transformations.\n\nCreate dashboards and automated scorecards for performance and key recommendation metrics.\n\nPartner with company and engineering leadership on priorities, and help grow the ML function over time.\n\nWhat We're Looking For\nAt least 3 years of experience with recommendation, ranking, or advertising systems, and typically 5 to 16 years of overall experience.\n\nProduction experience building a recommendation or matching system across candidate generation, ranking, embedding-based retrieval, serving, and evaluation.\n\nExperience with ad ranking, click-through or conversion prediction, or ad matching.\n\nHands-on experience with embeddings and real-time vector search tools such as FAISS, Milvus, Pinecone, or pgvector.\n\nStrong SQL skills and experience building data pipelines and transformation layers with dbt or similar tools, plus Postgres or analytical databases.\n\nAbility to work independently in a high-autonomy environment; early-stage startup experience and prior ML team leadership are useful.\n\nCompensation & Benefits\nBase salary: $200,000 to $300,000 annually. Visa sponsorship is not available.\nLocation\nHybrid, based in New York, New York. 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