Lead ML Engineer

Machine Learning Engineer

Newfold Digital is a leading web technology company serving millions of customers globally. Our customers know us through our robust portfolio of brands. We have some of the industry's most prominent and storied go-to-market brands, including Bluehost, HostGator, Network Solutions, Domain.com, Register.com, and Web.com. We help customers of all sizes build a digital presence that delivers results. With our extensive product offerings and personalized support, we take pride in collaborating with our customers to serve their online presence needs. The strength of our company lives in the intersection of our people, our customers, and our brands.

What You'll Do & How You'll Make Your Mark:

• Partner with the business to translate requirements into clear problem statements, KPIs, and experiment plans (A/B, holdout, backtests).
• Design data & ML architectures on lakehouse/warehouse stacks (e.g., Oracle Exadata, Spark/Databricks; Snowflake/BigQuery/Redshift with open table formats like Iceberg/Delta/Hudi or equivalent).
• Build pipelines for ingestion, feature engineering, and training (batch & streaming) using Python + SQL with orchestration (Airflow/Prefect/Dagster).
• Model using scikit-learn/XGBoost/LightGBM and PyTorch/TensorFlow; manage experiments and lineage.
• Serve & operate models on a major cloud ML platform (Azure ML, SageMaker or Vertex AI), with CI/CD, canary/blue-green, and rollback guardrails.
• Monitor & improve: implement data/model quality and drift monitoring, alerting, and dashboards; close the loop with BI (Power BI/Tableau/Looker).
• Document & review: author concise design docs and run technical reviews; mentor engineers; champion responsible AI practices.

Who You Are & How You'll Make Your Mark:

• 8+ years in applied ML & data engineering (3+ years leading delivery of production ML systems).
• Python expert with production-grade SQL; strong with pandas/Polars, scikit-learn, and one of: XGBoost/LightGBM.
• Fluency in core ML toolkits including TensorFlow, PyTorch, scikit-learn, and familiarity with Hugging Face or equivalent frameworks.
• Proven record of constructing and maintaining scalable data pipelines—both batch and streaming—for model training and deployment.
• Data platforms: hands-on with one of: Oracle ExaData, Spark/Databricks, or Snowflake, BigQuery/Redshift or equivalent; comfortable with open table formats (Iceberg/Delta/Hudi).
• Orchestration: real projects using one of Airflow, Prefect, or Dagster.
• Cloud ML platform: production deployments on one of SageMaker, Vertex AI, or Azure ML (pipelines, endpoints, registries).
• MLOps: CI/CD for ML, experiment tracking, model registry, observability (latency, errors), and data/model drift monitoring.
• Communication: ability to frame trade-offs and influence cross-functional partners; crisp writing of design/decision docs.