Founding Machine Learning Engineer

Mundi

Boston, Massachusetts, United States

Boston, Massachusetts, United States

À propos

About Onescreen Onescreen is the modern platform for out-of-home advertising — making it easier for brands and agencies to plan, buy, and measure OOH campaigns across thousands of vendors and formats. We move fast, operate lean, and hold ourselves to a high standard on every campaign we run. About the role You'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. What you’ll do Design and ship matching and ranking models for OOH inventory: candidate generation, re-ranking, geospatial-aware scoring. Own the data warehouse layer end to end: staging, marts, feature pipelines, freshness, lineage. Stand up offline and online evaluation infrastructure — measure the gap between them, don’t assume it. Publish ranking and matching APIs for product surfaces, with latency and quality SLOs. Instrument model monitoring: drift detection, prediction distribution, feature freshness, retraining triggers. Qualifications The 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. Beyond that: Strong production Python (NumPy, Pandas, FastAPI, SQLAlchemy). Strong SQL and modern data warehouse experience (BigQuery preferred). Real ranking and matching modeling fluency — learning-to-rank, retrieval and re-rank patterns, not just classification. Evaluation methodology rigor: holdouts, leakage prevention, online vs. offline gap measurement. Comfort owning the data pipeline as well as the model. Bias toward shipping. Clear writer. Self-directed. Nice to have Geospatial data experience (H3, PostGIS, GeoPandas) Mobility or location data experience Embedding-based retrieval (pgvector, FAISS, vector databases) Bandits, contextual bandits, or online learning A/B testing infrastructure design Causal inference dbt Ad-tech or OOH domain familiarity
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Boston, Massachusetts, United States

Compétences linguistiques

English

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