Define architecture and long-range technical strategy for ML systems and enabling platforms (training/inference orchestration, feature foundations, serving patterns, interoperability layers) across the measurement ecosystem
Lead development and productionization of next-generation ML approaches for identity resolution, audience modeling, and cross-platform measurement; translate advanced algorithms into scalable, production-quality implementations
Guide major data engineering and platform investments required for ML: distributed processing patterns, data routing/storage strategies, data integrity controls, and automation across internal/external data sources
Establish enterprise-grade MLOps, governance, and assurance processes: CI/CD standards, automated evaluation, model versioning/registry practices, drift detection, monitoring, and operational excellence
Lead cross-organization technical decision-making: align stakeholders, define success metrics, and drive complex trade-offs to deliver durable, scalable ML solutions
Coach and develop senior technical talent: mentor Staff/Senior engineers, set engineering standards, and build communities of practice
Champion privacy, security, and responsible AI: ensure privacy-by-design, PII safeguards, and audit readiness (GDPR/CCPA) for ML and data workflows

Requirements

Must have 10+ years of professional experience delivering production ML systems at scale, including significant ownership of architecture and platform strategy
Must have expert coding skills in Python and strong SQL; proven software engineering maturity (testing, CI/CD, design reviews, documentation)
Demonstrated ability to apply ML techniques in code to build predictive systems at scale (including deep learning where appropriate)
Proven ability to influence across teams and drive organizational standards for data/ML reliability, governance, and interoperability
Deep experience with distributed compute/data platforms and performance optimization
12+ years total experience, with hands-on work in media, advertising technology, or cross-platform audience measurement
Flagship production experience with deep-learning, genAI, or retrieval-augmented systems (PyTorch, vector databases) and real-time data pipelines (Kafka, Pub/Sub, Kinesis)
Strong understanding with modern MLOps stacks (e.g., MLflow, Kubeflow, Vertex AI, SageMaker) and model-governance practices (metadata, lineage, drift detection)
Certifications such as Google Professional Machine Learning Engineer, AWS Certified Machine Learning – Specialty, or equivalent cloud/data credentials
Contributions to open-source ML or data-engineering projects, conference presentations, or peer-reviewed publications
Experience in media/ad tech, identity graphs, audience measurement, or interoperability layers
Experience with modern MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker) and model governance practices

Benefits

A bonus and/or long-term incentive units may be provided as part of the compensation package
Full range of medical, financial, and/or other benefits, dependent on the level and position offered

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonSQLmachine learningdeep learningMLOpsdata engineeringdistributed processingautomationmodel versioningdata integrity

Soft Skills

leadershipmentoringstakeholder alignmentinfluencedecision-makingcoachingorganizational standardscommunicationcollaborationtechnical guidance

Certifications

Google Professional Machine Learning EngineerAWS Certified Machine Learning – Specialty