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.
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.
- 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
Skills Required
- Owned a production ranking, matching, or recommendation system end-to-end
- Production Python (NumPy, Pandas) experience
- FastAPI and SQLAlchemy experience
- Strong SQL and modern data warehouse experience (BigQuery preferred)
- Fluency with ranking and matching models (learning-to-rank, retrieval and re-rank patterns)
- Evaluation methodology rigor (holdouts, leakage prevention, online vs offline gap measurement)
- Comfort owning data pipelines as well as models (staging, marts, feature pipelines, freshness, lineage)
- Bias toward shipping; clear writing; self-directed work style
- 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 out-of-home (OOH) domain familiarity
What We Do
Onescreen was started to help marketers add effective real-time marketing through strategically planned and executed out-of-home advertising. From billboards, blimps, and buses to wrapped cars and connected TVs in bars and restaurants, the one thing we focus on is planning and delivering the most efficient OOH plans.
