Founding Machine Learning Engineer

About Onescreen

Mission

We give growing brands the confidence, defensibility, and predictability to buy and execute real world advertising like the strategic channel it is.

Vision

Be the strategic partner who has built the intelligence layer for brands to confidently extend their omnichannel campaigns into the real world.

Values - how we work

1. We obsess over the customer's real problem
2. Chase, say, do
3. Move with urgency and intention
4. Build with transparency
5. Become the standard
6. Bring the juice

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