Senior ML Engineer - Ads & Search, Low-Latency + Equity

adMarketplace; New York (NY); On-site; USD 170,000 - 240,000; Full time

Benefits offered by this job

Bonus

Equity

Job summary

adMarketplace is seeking an experienced Senior Machine Learning Engineer to help build and optimize ultra-low-latency ad-serving and search solutions in New York. You will work with product and engineering teams on relevancy, yield optimization, predictive modeling, and bidding performance.

Responsibilities include end-to-end ML project lifecycle, MLOps, model architecture decisions, feature pipelines, and A/B testing.

Qualifications

• PhD with 3+ years or MS with 3-5+ years in AI/ML production systems.
• 2+ years in AI/ML models within ads, relevance, ranking, or search.
• 3+ years building distributed, low-latency ML services.
• 3+ years deploying ML pipelines with feature engineering and monitoring.
• 2+ years Python and distributed frameworks (Spark, Hadoop), SQL, cloud.

Responsibilities

• Drive end-to-end lifecycle of AI/ML projects from data collection to deployment and maintenance.
• Implement MLOps practices including pipelines, monitoring, and QA for model reliability.
• Contribute to model architecture with state-of-the-art techniques.
• Develop and deploy low-latency ML services and feature pipelines.
• Establish and run robust A/B testing to improve model performance.
• Translate research into production-ready code and collaborate across teams.

Skills

Python

Distributed frameworks

SQL

TensorFlow

PyTorch

Scikit-learn

Spark

Hadoop

Low latency

MLOps

Reinforcement learning

LLMs

Generative AI

Education

PhD or MS in AI/ML related field

Tools

Spark

Hadoop

Job description

adMarketplace is seeking an experienced Senior Machine Learning Engineer to help build and optimize ultra-low-latency ad-serving and search solutions in New York. You will work with product and engineering teams on relevancy, yield optimization, predictive modeling, and bidding performance.

Responsibilities include end-to-end ML project lifecycle, MLOps, model architecture decisions, feature pipelines, and A/B testing.