Senior Machine Learning Engineer

About MediaRadar

MediaRadar, now including the data and capabilities of Vivvix, powers the mission-critical marketing and sales decisions that drive competitive advantage. Our competitive advertising intelligence platform enables clients to achieve peak performance with always-on data and insights that span the media, creative, and business strategies of five million brands across 30+ media channels. By bringing the advertising past, present, and future into focus, our clients rapidly act on the competitive moves and emerging advertising trends impacting their business.

Job Summary:

We're continuing to build a best-in-class AI and Machine Learning team focused on delivering advanced capabilities that empower both our data organization and customers.

This team is responsible for developing scalable, intelligent systems that automate complex data workflows, improve data quality, and enable smarter insights through cutting-edge AI, LLM, and retrieval technologies.

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.

Stack highlights: PostgreSQL + pgvector, LangChain, Azure OpenAI, SQLAlchemy/Alembic, Pydantic, pytest, async I/O.

Responsibilities:

Retrieval & Relevance

• 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.

Model Development & Evaluation

• 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).

Data Engineering & Infrastructure

• 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.

Operational Excellence

• 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.

Success Measures:

• Retrieval Performance: Demonstrable improvements in model recall, precision, and fusion quality.
• System Reliability: Scalable, high-throughput ingestion and vectorization with minimal downtime.
• Model Impact: Proven improvement in automation, deduplication, or classification accuracy.
• Code Quality: Robust, well-tested, and maintainable codebase with strong documentation.
• Operational Efficiency: Faster iteration cycles, reproducibility, and measurable performance gains.

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.

Career Path:

At MediaRadar, a Senior Machine Learning Engineer role is more than a technical position it's a foundation for growing into leadership and specialized roles within our expanding AI and Data Innovation organization. Engineers who perform successfully will have the opportunity to grow into roles such as:

• Staff Machine Learning Engineer: Drive the architecture and scalability of our ML pipelines, retrieval systems, and LLM-powered solutions across multiple data domains.
• MLOps Architect: Design and operationalize the infrastructure that supports continuous model training, evaluation, deployment, and observability at scale.
• Applied Scientist: Research and implement advanced AI techniques, such as fine-tuned LLMs, embeddings, and multimodal retrieval models, to create new capabilities for our data and customers.
• AI Product Engineer: Partner with product and data teams to transform machine learning models into production-grade, customer-facing features that power innovation and insight.
• Engineering Manager, AI/ML: Lead teams of engineers and data scientists, setting strategy and driving excellence in delivery, quality, and collaboration across the AI and Data organization.

With exposure to real-world data challenges, mentorship from senior leaders, and opportunities to contribute to high-impact AI initiatives, Machine Learning Engineers can expect to expand their expertise and progress into roles aligned with their technical strengths and career aspirations.