Senior Data & Machine Learning Engineer at SIVAD Consulting

Job description

Senior Data & Machine Learning Engineer – NYC (Hybrid) PTQ $200k - $300k 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. Responsibilities • 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. • Lead 0-to-1 initiatives from prototype to production, including online learning and feedback-loop systems, reliable ETL/ELT pipelines, and the dbt/warehouse transformation layer • Dashboards and automated scorecards giving the team and leadership real-time visibility into performance, CTR lift, tag coverage, and other key metrics. • Work closely with the CTO, CEO, and engineering team to set priorities and the order of work for scaling the recommendation system • Grow into a leadership role, with a path to building and running an ML team Work Experience and Requirements • 3+ years of experience with recommendation, ranking, or ads systems • 5 - 16 years of experience in production ML or data engineering • Has owned multiple layers of a recommendation system end-to-end (retrieval, ranking, serving, evaluation), not one component on a large team • Recsys models for ads: ads ranking, CTR or conversion prediction, or ad matching • Embeddings and real-time vector-search infrastructure (FAISS, Milvus, Pinecone, pgvector or similar) • Founder background or early-stage startup experience • Based in the NYC area, OR in a US time zone close to Eastern, and can travel to NYC quarterly • Built and maintained their own data pipelines and transformation layer in SQL and dbt (or similar) • Previous experience building an ML team • • Education • Master's or Ph.D. in Computer Science / Machine Learning preferred (helpful but not required)

Requirements

Must-haves

Senior Data & Machine Learning Engineer – NYC (Hybrid) PTQ
$200k - $300k
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.
Lead 0-to-1 initiatives from prototype to production, including online learning and feedback-loop systems, reliable ETL/ELT pipelines, and the dbt/warehouse transformation layer
Dashboards and automated scorecards giving the team and leadership real-time visibility into performance, CTR lift, tag coverage, and other key metrics.
Work closely with the CTO, CEO, and engineering team to set priorities and the order of work for scaling the recommendation system
Grow into a leadership role, with a path to building and running an ML team
Work Experience and Requirements
3+ years of experience with recommendation, ranking, or ads systems
5 - 16 years of experience in production ML or data engineering
Has owned multiple layers of a recommendation system end-to-end (retrieval, ranking, serving, evaluation), not one component on a large team
Recsys models for ads: ads ranking, CTR or conversion prediction, or ad matching
Embeddings and real-time vector-search infrastructure (FAISS, Milvus, Pinecone, pgvector or similar)
Founder background or early-stage startup experience
Based in the NYC area, OR in a US time zone close to Eastern, and can travel to NYC quarterly
Built and maintained their own data pipelines and transformation layer in SQL and dbt (or similar)
Previous experience building an ML team

Nice-to-haves

Master's or Ph.D. in Computer Science / Machine Learning preferred (helpful but not required)

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlsql

Platform: manatal
Effort: medium