ABOUT PATTERNIQ
PatternIQ is an AI-powered consumer world model that simulates customer behavior at civilization scale. We’re building a continuously updating model of billions of people that predicts what they want, the content they’ll engage with, what they’ll buy, and how they will respond to any changes businesses want to simulate. This is critical infrastructure for advertising, commerce, pricing, product development, and eventually any AI system making decisions about consumers.
Our ML-driven programmatic ad network, InboxMatch, delivers unique ads to unique recipients inside a publisher's email campaigns. We replace the status-quo, one-size-fits-all static placement with hyper-personalized ads powered by a self-learning recommendation engine.
We're seed-stage and growing quickly. The culture is meritocratic and intense. We expect everyone to do their best work, raise the bar of the team they join, and act like owners. Debate is open and direct. We let data settle arguments.
THE ROLE
We're looking for a Senior Data & Machine Learning Engineer who can independently own the matching stack end-to-end. You'll work directly with the engineering team to build the models, the serving path, and the platform underneath them.
This is a high-autonomy position. You won't be handed a roadmap of analyses to run. You'll look at the data, identify what matters, form your own hypotheses, and design experiments to test them. The best candidate thinks like a scientist and builds like an engineer.
WHAT YOU'LL OWN
- Retrieval, ranking, and re-ranking for ad-to-recipient matching.
- The embedding and vector-search infrastructure powering real-time ad selection inside publisher newsletters.
- Multi-objective optimization in a single ranking stack: CTR lift, advertiser ROAS, publisher revenue, and subscriber relevance.
- The feature store and the serving path: offline training, online inference, and the parity between them. Pipeline health is your problem, not someone else's.
- LLMs and foundation models applied to subscriber tagging, content understanding, and user modeling.
- Data pipelines that turn raw event data into reliable, queryable, experiment-ready datasets.
- The warehouse and transformation layer.
- Experimentation end-to-end: find the high-value questions, design statistically sound A/B and multivariate tests, instrument collection, analyze, and recommend.
- Dashboards and automated scorecards giving the team and leadership real-time visibility into performance, CTR lift, tag coverage, and other key metrics.
- 0 to 1 initiatives from prototype to production.
- Clean data contracts and well-designed schemas, in partnership with engineering.
WHAT WE'RE LOOKING FOR
- 5+ years of production data or ML engineering experience where you owned outcomes, not just deliverables.
- Production recommendation or matching systems: candidate generation, ranking models, and embedding-based retrieval. You've shipped a system that does all three layers, not just one.
- Vector database experience in production, not a prototype.
- Strong SQL and data modeling. You're fluent in Postgres and comfortable across columnar analytical databases.
- Hands-on dbt experience. You've built and maintained transformation layers, written tests, and used dbt (or similar) as a core part of a production stack.
- Data pipeline engineering chops. You can build reliable ETL/ELT workflows, not just consume their output.
- LLMs applied to structured prediction. You're comfortable using foundation models for tagging, enrichment, and classification.
- Online learning and feedback-loop systems, where the next training cycle depends on yesterday's serving logs.
- Experiment design rigor. You understand statistical significance, sample sizing, and the difference between a compelling story and a valid conclusion.
- Independent, self-directed working style. You don't wait for someone to define the question. You find it, scope it, and go after it.
- Comfort with ambiguity and imperfect data. You know how to work with what's available while building toward what's ideal.
Preferred: strong recsys foundations, or a background in social, marketplace, or streaming recommendations. Matching individuals to ads is a different problem than matching emails to ads, and consumer-platform recsys experience generalizes well here.
HOW WE WORK
- Small, distributed team that values results.
- Direct line to the CTO and CEO. Your analysis and recommendations will directly influence product and business decisions.
- We use AI tools aggressively and expect everyone to push the boundary of what's possible with AI-assisted work.
- We do more with less. Efficient use of infrastructure and tooling isn't a nice-to-have, it's how we operate.