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. Backed by Techstars.
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 they're looking for
- 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
- Strong production Python (NumPy, Pandas, FastAPI, SQLAlchemy)
More about this role
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...
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