Principal Machine Learning Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop and sustain artificial intelligence and machine learning systems for the organization

• Address intricate machine learning challenges

• Create and execute data architectures and models

• Advance MLOps and LLMOps capabilities

• Oversee and enhance machine learning systems

• Collaborate across project and product teams to eliminate obstacles, expedite, and enhance delivery

• Guide engineers at all levels

• Establish principles for ML engineering and architecture

• Influence the comprehensive architecture, development, and deployment of AI, machine learning, GenAI, and agentic models

• Design and construct agentic AI systems featuring multi-agent orchestration, tool utilization, evaluation, and autonomous task-execution pipelines

• Uphold standards for data preprocessing, feature engineering, model selection, hyperparameter tuning, evaluation, and documentation

• Collaborate with data engineering teams to create robust, scalable data pipelines for model training and inference

• Implement CI/CD pipelines for data wrangling, feature engineering, model training, evaluation, deployment, and monitoring

• Define and uphold standards for model versioning, experiment tracking, reproducibility, and model registry management

• Establish monitoring for performance degradation, data drift, concept drift, and production bias, including automated alerts and retraining triggers

• Create AI governance frameworks that address model risk management, fairness, explainability, privacy, and security

• Advocate for AI principles, human oversight, and accountability throughout the AI development lifecycle

• Report to the Senior Director of Data Science & AI

⛳️ Requirements

• Over 10 years of experience building software or machine learning systems, with at least 6 years in deploying and operating machine learning in production

• Previous experience as the senior engineer or near-senior engineer on a production ML or data platform, responsible for architectural decisions

• Proven history of establishing technical standards and leading through influence without formal authority

• Strong hands-on expertise in Python and SQL

• Solid understanding of software engineering principles, including version control, automated testing, code review, and modular design

• Expert-level hands-on experience with Databricks, encompassing Spark, Lakeflow Declarative Pipelines, Delta Lake, Lakebase/Postgres, Unity Catalog, and Model Serving

• Experience with the Azure data ecosystem

• Familiarity with MLflow or a similar tool for experiment tracking, model registry, tracing, and reproducibility

• At least one year of experience building agentic systems that have achieved production use, or equivalent demonstrated work

• Proven ability to mentor engineers across all levels and enhance technical standards

• Understanding of contemporary machine learning frameworks and reusable, scalable engineering patterns

• Preferred: experience in developing an ML or AI platform capability

• Preferred: background working across multiple teams, locations, and time zones

• Preferred: knowledge of CI/CD and infrastructure-as-code tools such as Azure DevOps, Databricks Declarative Automation Bundles, GitHub Actions, Terraform, or similar

• Preferred: understanding of data governance, metadata management, and cataloging

• Preferred: experience with model serving and inference at scale, feature stores, retrieval pipelines, or LLM cost-attribution tools

• Preferred: familiarity with Model Context Protocol or similar tool-integration standards

• Preferred: experience in commercial real estate, professional services, or similarly data-intensive domains

🏝️ Benefits

• Health insurance

• Vision insurance

• Dental insurance

• Flexible spending accounts

• Health savings accounts

• Retirement savings plans

• Life insurance

• Disability insurance programs

• Paid time away from work

• Unpaid time away from work