Sr. ML Engineer

2 months ago

Remote, WorldwideSenior

Responsibilities

Convert data science prototypes for propensity, lookalike, segmentation, and fusion use cases into reproducible production ML services.
Build and operate large-scale data and feature pipelines on Databricks and Spark, using Snowflake to produce versioned audience segments on schedule.
Own the model lifecycle, including MLflow tracking and registry, CI/CD, automated retraining, drift monitoring, and rollback.
Engineer governed activation-layer delivery of segments and scores into DSPs, SSPs, DMPs/CDPs, clean rooms, CTV, social, and programmatic partners.
Implement identity resolution, audience onboarding, and delivery integrations across advertising platforms.
Design scalable, low-latency, cost-efficient, and reproducible batch and near-real-time scoring systems on Azure.
Apply privacy-by-design practices including data minimization, access controls, encryption, and clean-room-compatible patterns.
Set engineering standards, lead end-to-end system design, and mentor other engineers.

8–9 years of hands-on ML engineering experience shipping and operating production systems at scale.
Prior experience in media, advertising, or audience activation.
Production experience with Databricks, MLflow, Spark, Snowflake, and Azure services including ADLS, Azure ML, Azure DevOps, and AKS.
Expert Python and ML ecosystem skills with strong SQL at scale.
Strong applied statistics, such as sampling and weighting, forecasting, causal inference, or experimental design.
A CS or engineering degree with strong quantitative foundations.
Pragmatic, hands-on production judgment and the ability to communicate with non-expert stakeholders.
Experience mentoring engineers and working effectively in an English-language business environment.
Preferred experience with Docker, Kubernetes, Structured Streaming, Databricks tuning, Model Serving, Feature Store, Databricks Workflows, Airflow, Dagster, ad-tech identity systems, DSP/SSP, DMP/CDP, clean rooms, identity graphs, onboarding, and delivery APIs.
AWS or GCP experience is a plus.

Remote, full-time position.
Requires working the second half of the day to overlap with a US-based product team.