{"schemaVersion":"jobsearcher.job.v1","id":"5a2f9c6fe0eaf82db89a5cbd","url":"https://jobsearcher.com/jobs/5a2f9c6fe0eaf82db89a5cbd","canonicalUrl":"https://jobsearcher.com/jobs/5a2f9c6fe0eaf82db89a5cbd","title":"Founding Machine Learning Engineer","description":"About Onescreen\n\nMission\n\nWe give growing brands the confidence, defensibility, and predictability to buy and execute real world advertising like the strategic channel it is.\n\nVision\n\nBe the strategic partner who has built the intelligence layer for brands to confidently extend their omnichannel campaigns into the real world.\n\nValues - how we work\n\nWe obsess over the customer's real problem\nChase, say, do\nMove with urgency and intention\nBuild with transparency\nBecome the standard\nBring the juice\n\nAbout the role\n\nYou'll be the founding ML engineer who owns our matching algorithms from exploration through production and the data platform that feeds them. You'll design and ship the models that rank OOH inventory against advertiser personas, markets, and dayparts. You'll own our data warehouse shape and the pipelines that fill it. You'll publish the ranking and matching APIs that downstream products, agents, and automation surfaces consume.\n\nWhat you'll do\n\nDesign and ship matching and ranking models for OOH inventory: candidate generation, re-ranking, geospatial-aware scoring.\nOwn the data warehouse layer end to end: staging, marts, feature pipelines, freshness, lineage.\nStand up offline and online evaluation infrastructure — measure the gap between them, don't assume it.\nPublish ranking and matching APIs for product surfaces, with latency and quality SLOs.\nInstrument model monitoring: drift detection, prediction distribution, feature freshness, retraining triggers.\nQualifications\n\nThe hard requirement: you have owned a production ranking, matching, or recommendation system end-to-end. You chose the model, designed the features, made the evaluation methodology calls, and were on the hook when it drifted. We care about that ownership scope more than years on a résumé — title and compensation are scaled to your demonstrated expertise.\n\nBeyond that:\n\nStrong production Python (NumPy, Pandas, FastAPI, SQLAlchemy).\nStrong SQL and modern data warehouse experience (BigQuery preferred).\nReal ranking and matching modeling fluency — learning-to-rank, retrieval and re-rank patterns, not just classification.\nEvaluation methodology rigor: holdouts, leakage prevention, online vs. offline gap measurement.\nComfort owning the data pipeline as well as the model.\nBias toward shipping. Clear writer. Self-directed.\nNice to have\nGeospatial data experience (H3, PostGIS, GeoPandas)\nMobility or location data experience\nEmbedding-based retrieval (pgvector, FAISS, vector databases)\nBandits, contextual bandits, or online learning\nA/B testing infrastructure design\nCausal inference\ndbt\nAd-tech or OOH domain familiarity","company":"Onescreen","rawCompany":"onescreen","city":"Chicago","state":"IL","isRemote":false,"isActive":false,"createdAt":"2026-11-04T10:50:16.605Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Founding Machine Learning Engineer","description":"About Onescreen\n\nMission\n\nWe give growing brands the confidence, defensibility, and predictability to buy and execute real world advertising like the strategic channel it is.\n\nVision\n\nBe the strategic partner who has built the intelligence layer for brands to confidently extend their omnichannel campaigns into the real world.\n\nValues - how we work\n\nWe obsess over the customer's real problem\nChase, say, do\nMove with urgency and intention\nBuild with transparency\nBecome the standard\nBring the juice\n\nAbout the role\n\nYou'll be the founding ML engineer who owns our matching algorithms from exploration through production and the data platform that feeds them. You'll design and ship the models that rank OOH inventory against advertiser personas, markets, and dayparts. You'll own our data warehouse shape and the pipelines that fill it. You'll publish the ranking and matching APIs that downstream products, agents, and automation surfaces consume.\n\nWhat you'll do\n\nDesign and ship matching and ranking models for OOH inventory: candidate generation, re-ranking, geospatial-aware scoring.\nOwn the data warehouse layer end to end: staging, marts, feature pipelines, freshness, lineage.\nStand up offline and online evaluation infrastructure — measure the gap between them, don't assume it.\nPublish ranking and matching APIs for product surfaces, with latency and quality SLOs.\nInstrument model monitoring: drift detection, prediction distribution, feature freshness, retraining triggers.\nQualifications\n\nThe hard requirement: you have owned a production ranking, matching, or recommendation system end-to-end. You chose the model, designed the features, made the evaluation methodology calls, and were on the hook when it drifted. We care about that ownership scope more than years on a résumé — title and compensation are scaled to your demonstrated expertise.\n\nBeyond that:\n\nStrong production Python (NumPy, Pandas, FastAPI, SQLAlchemy).\nStrong SQL and modern data warehouse experience (BigQuery preferred).\nReal ranking and matching modeling fluency — learning-to-rank, retrieval and re-rank patterns, not just classification.\nEvaluation methodology rigor: holdouts, leakage prevention, online vs. offline gap measurement.\nComfort owning the data pipeline as well as the model.\nBias toward shipping. Clear writer. Self-directed.\nNice to have\nGeospatial data experience (H3, PostGIS, GeoPandas)\nMobility or location data experience\nEmbedding-based retrieval (pgvector, FAISS, vector databases)\nBandits, contextual bandits, or online learning\nA/B testing infrastructure design\nCausal inference\ndbt\nAd-tech or OOH domain familiarity","datePosted":"2026-11-04T10:50:16.605Z","dateModified":"2026-11-04T10:50:16.605Z","hiringOrganization":{"@type":"Organization","name":"Onescreen","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Chicago","addressRegion":"IL","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"5a2f9c6fe0eaf82db89a5cbd"},"url":"https://jobsearcher.com/jobs/5a2f9c6fe0eaf82db89a5cbd"}}