Senior Data & Machine Learning Engineer – NYC (Hybrid) PTQ

Sivadconsulting; New York (NY); Hybrid; USD 200,000 - 300,000; Full time

Job summary

Sivadconsulting in New York City (Hybrid) seeks a Senior Data & Machine Learning Engineer to own the matching stack end-to-end. You will build models, the serving path, and the underlying platform, with high autonomy and a focus on experiments and scientific thinking.

The role requires 3+ years in recommender/ads systems and 5–16 years in production ML or data engineering, plus experience with embedding/vector-search tech. You may lead ML initiatives and evolve into a leadership role.

Qualifications

• 3+ years of experience with recommendation, ranking, or ads systems.
• 5-16 years of experience in production ML or data engineering.
• Owned multiple layers of a recommendation system end-to-end (retrieval, ranking, serving, evaluation).
• Experience building ML pipelines and data transformations in SQL/dbt or similar.

Responsibilities

• Own the matching stack end-to-end from data to serving and evaluation.
• Develop models, serving paths, and the platform underneath them.
• Lead 0-to-1 initiatives including online learning and reliable ETL/ELT pipelines.
• Build dashboards and scorecards to track CTR lift, ROAS, revenue, and relevance.
• Collaborate with CTO/CEO to set priorities for scaling the recommendation system.
• Grow into a leadership role and potentially lead an ML team.

Skills

Recommendation systems

Ads ranking

Production ML

Data engineering

Education

Master's or Ph.D. in Computer Science / Machine Learning

Tools

FAISS

Milvus

Pinecone

pgvector

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)