Lead Machine Learning Engineer

Lead Machine Learning Engineer

This is not a remote role. You must be in the local area or willing to relocate.

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.

• 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).

• 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/Big Query) and container orchestration (Docker/Kubernetes).
• Proven ability to lead technical initiatives across teams and influence architecture and standards.

• 8+ years total experience, with hands‑on work in media, advertising technology, or cross-platform audience measurement.
• Strong understanding of modern MLOps stacks (e.g., MLflow, Kubeflow, Vertex AI, Sage Maker) 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, Sage Maker) and model governance practices.

• Bachelor's degree in a relevant technical or science field (e.g., computer science, data science, mathematics, or a related discipline).

• Master’s degree or PhD in a relevant field (e.g., Applied Math, Computer Science, Computational Science, Operation Research, Data Science).

The hiring range for this position in New York City is $ - $ per year. The base pay actually offered…