Senior Machine Learning Engineer

November 12

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MediaRadar, Inc.

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Advertising • SaaS

MediaRadar, Inc. is an award-winning advertising intelligence platform designed to support media sales and advertising planning. With over 4 million brands and comprehensive contact information, MediaRadar is a go-to solution for media sellers and buyers, providing insights into advertising data across various formats. The platform is widely used in the media and ad tech industry for prospecting, media buying, planning, and sales enablement. MediaRadar leverages AI-powered solutions to deliver actionable advertising intelligence, helping users make informed decisions for better media mix and revenue growth.

201 - 500 employees

Founded 2007

☁️ SaaS

📋 Description

• As a Senior Machine Learning Engineer, you’ll be a key contributor in designing, implementing, and optimizing machine learning solutions that power our data products and enhance our customers’ experience. This is a hands-on role for someone who enjoys solving technically challenging problems at the intersection of data, engineering, and AI. • **Responsibilities:** • - Improve retrieval quality through scoring optimization, fusion methods (RRF vs weighted), and query normalization. • - Implement heuristics and relevance-tuning logic to enhance matching precision and recall. • - Design and evaluate hybrid retrieval workflows combining semantic and lexical search. • - Build, fine-tune, and evaluate LLM-based agents for classification, deduplication, and decision-making tasks. • - Develop pipelines to measure accuracy, precision, recall, and model reliability. • - Implement guardrails, thresholds, and fallback logic to ensure consistent, explainable results (Langfuse observability). • - Optimize data vectorization and ingestion jobs (batching, concurrency, retry logic, and backfills). • - Maintain ORM models and database migrations using SQLAlchemy + pgvector and Alembic. • - Ensure data schema consistency and efficient vector indexing with pgvector. • - Develop clean, scalable ETL/ELT workflows to support data enrichment and ML readiness. • - Create observability tools, logging, and metrics dashboards to support production ML systems. • - Produce reviewer-friendly exports, lightweight CLIs, and analytical reports for QA and ops teams. • - Contribute to documentation, design standards, and operational best practices for ML pipelines.

🎯 Requirements

• **Key Qualifications and Role Requirements:** • - Expert Python engineering skills — strong understanding of typing, packaging, async I/O, and performance optimization. • - Deep PostgreSQL expertise — SQL, indexing (pg_trgm, ivfflat/hnsw), and query plan optimization. • - Proficiency in machine learning system design with emphasis on retrieval, RAG, or LLM-based architectures. • - Experience with LangChain, OpenAI/Azure OpenAI, or equivalent LLM frameworks. • - Strong testing and evaluation mindset (pytest, metrics, eval harnesses). • - Hands-on experience with LLM agents and Retrieval-Augmented Generation (RAG) pipelines. • - Familiarity with asyncio or ThreadPoolExecutor for concurrent I/O-bound processes. • - Experience with Docker, devcontainers, or Kubernetes for scalable deployments. • - Background in observability, metrics logging, or offline evaluation frameworks (e.g., Langfuse). • - Exposure to both relational and NoSQL databases (PostgreSQL, MongoDB). • - Experience integrating ML components into production-grade APIs or services.

🏖️ Benefits

• At MediaRadar, we are committed to creating an inclusive and accessible workplace where everyone can thrive. We believe that diversity of backgrounds, perspectives, and experiences makes us stronger and more innovative. We are proud to be an Equal Opportunity Employer and make employment decisions without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, or any other legally protected status. This is a full-time exempt role with base salary plus benefits. Final compensation will depend on location, skill level, and experience.