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
Skills Required
- Owned a production ranking, matching, or recommendation system end-to-end (model choice, features, evaluation, ownership for drift).
- Production Python experience (NumPy, Pandas, FastAPI, SQLAlchemy).
- Strong SQL and modern data warehouse experience.
- Experience with BigQuery.
- Fluency in ranking and matching modeling (learning-to-rank, retrieval and re-rank patterns).
- Rigorous evaluation methodology knowledge (holdouts, leakage prevention, measuring online vs offline gaps).
- Comfort owning data pipelines, feature pipelines, and data warehouse design.
- Experience publishing and operating low-latency ranking/matching APIs with SLO awareness.
- Model monitoring and observability (drift detection, prediction distribution, feature freshness, retraining triggers).
- Bias toward shipping, clear writing, and self-direction.
- Geospatial data experience (H3, PostGIS, GeoPandas).
- Mobility or location data experience.
- Embedding-based retrieval experience (pgvector, FAISS, vector databases).
- Bandits, contextual bandits, or online learning knowledge.
- A/B testing infrastructure design experience.
- Causal inference knowledge.
- dbt experience.
- 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.
.png)
