About the job
The Lead Machine Learning Engineer is a senior individual contributor who provides technical leadership for complex machine learning systems and the data foundations required to operate them. This role applies machine learning techniques in code (e.g., supervised/unsupervised learning, deep learning/neural networks where appropriate, and advanced modeling approaches) to build predictive systems at scale for identity, audience, and cross-platform measurement. The position also leads architecture and standards for ML pipelines that capture, manage, store, and utilize large-scale structured and unstructured data, ensuring data integrity, interoperability, and reliability across production environments.
Responsibilities
Lead development, training, and deployment of advanced ML models for identity resolution, look-alike modeling, and cross-platform measurement; translate algorithms into production-quality code; optimize for scale and performance.
Architect scalable ML platforms and reusable components (training/inference pipelines, feature/label foundations, model serving patterns) that operate across distributed cloud and platform environments
Lead data and feature foundations: define data contracts, metadata/lineage expectations, and automated quality controls to maintain data integrity across structured/unstructured sources in Snowflake/Databricks.
MLOps & reliability: establish CI/CD patterns, model versioning/registry practices, automated evaluation, drift detection, monitoring dashboards/alerts, and operational playbooks for sustained production health.
Cross-functional technical leadership: drive design reviews, clarify technical requirements, and lead multi-quarter initiatives with product, analytics, and platform engineering stakeholders.
Mentorship & enablement: mentor engineers through code/design reviews; build shared libraries and best practices to improve team velocity and quality.
Privacy, governance & compliance: ensure privacy-by-design practices, PII safeguards, documentation, and audit readiness across ML workflows (GDPR/CCPA).
Qualifications
Minimum
Must have strong production experience with deep-learning, genAI, or retrieval-augmented systems (PyTorch, vector databases) and real-time data pipelines (Kafka, Pub/Sub, Kinesis)
Must have 7+ years of professional experience delivering production ML systems (models + pipelines + monitoring) at scale
Must have advanced coding skills in Python and SQL; strong software engineering discipline (testing, CI/CD, code review, design documentation)
Must have demonstrated experience applying ML techniques in code to develop predictive systems at scale (including deep learning where appropriate)
Must have hands-on expertise with cloud-native data platforms and distributed compute (Snowflake/Databricks/Spark/BigQuery) and container orchestration (Docker/Kubernetes)
Proven ability to lead technical initiatives across teams and influence architecture and standards
Preferred
8+ years total experience, with hands-on work in media, advertising technology, or cross-platform audience measurement
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, Ver