Senior Data & Machine Learning Engineer
Summary
Senior engineer owning the ad-matching/recommendation stack end to end at a small early-stage startup: retrieval, ranking, and re-ranking for personalized ads, embeddings and real-time vector search, LLM/foundation-model applications, plus feature store, training/inference pipelines, and dbt/SQL data infrastructure. Hybrid in NYC; $200–300k; no visa sponsorship.
About the Role
Join a small, early-stage AI team building a recommendation system for personalized advertising. You will own the matching stack end to end, from data and models through real-time serving, and help shape the system's technical priorities as it scales.
What You'll Do
Build retrieval, ranking, and re-ranking systems to match ads with recipients.
Develop embedding and vector-search infrastructure for real-time ad selection.
Optimize ranking across click-through rate, advertiser return, publisher revenue, and subscriber relevance.
Own the feature store, offline training, online inference, serving path, and pipeline health.
Apply large language models and foundation models to subscriber tagging, content understanding, and user modeling.
Take initiatives from prototype to production, including online learning, feedback loops, ETL/ELT pipelines, and warehouse transformations.
Create dashboards and automated scorecards to track performance and key metrics.
Partner with company leadership and engineering to set priorities, with an opportunity to grow into leading an ML team.
What We're Looking For
At least 3 years of experience with recommendation, ranking, or advertising systems, and 5 to 16 years of overall experience.
Production experience owning recommendation or matching systems across candidate generation, ranking, embedding-based retrieval, serving, and evaluation.
Experience building ad ranking, click-through or conversion prediction, or ad-matching models.
Hands-on experience with embeddings and real-time vector search tools such as FAISS, Milvus, Pinecone, pgvector, or similar.
Strong SQL skills and experience building data pipelines and transformation layers with dbt or similar tools, plus Postgres or columnar analytical databases.
Early-stage startup or founder experience is valued; experience building an ML team and graduate study in computer science or machine learning are also relevant.
Compensation & Benefits
Base salary: $200,000 to $300,000 annually. Visa sponsorship is not available.
Location
Hybrid, with preference for candidates in the New York City area or in a nearby U.S. time zone who can travel to New York City quarterly.
Skills
As published by ashby · 2 questions
Basics
Name, Email, Resume
Short answers (1)
- LinkedIn Profile URL optional
Pick from a list (1)
- Do you have work authorization for the country this role is based in? optional