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# Senior Data & Machine Learning Engineer

- **Company:** PatternIQ, Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Databases, Content Analysis, Extract Transform Load (ETL), Information Retrieval, PostgreSQL, Machine Learning, Operational Databases, Standard Sql, Large Language Models, Multi-objective Optimization, Data Pipelines
- **Published:** October 8, 2026
- **Apply:** https://www.disabledperson.com/jobs/75866472-senior-data-machine-learning-engineer

## About the Role

* 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.

## Description

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., * 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.