Machine Learning Engineer, Radar

• 5+ years building production ML systems at scale, including feature engineering, training, deployment, and monitoring
• Strong proficiency in Python and ML frameworks (scikit-learn, PyTorch, XGBoost)
• Hands-on experience with cloud ML platforms (AWS SageMaker, Vertex AI, or Azure ML)
• Expertise in big data processing including SQL optimization and distributed computing (Spark/Dask)
• Production experience with workflow orchestration tools (Airflow, Dagster, Prefect)
• Proficiency with version control (Git) and CI/CD practices
• Research has shown that women & underrepresented minorities are more likely to read lists of requirements and consider themselves unqualified if they don't meet every single one. This list represents what we're ideally looking for, but everyone has unique strengths & weaknesses, and we hire for strength & potential, not lack of weakness

Desirable

• We are looking for a Machine Learning Engineer to help build and develop our ML capabilities at RADAR. The role requires extensive collaboration with teams and functions across the company ranging from product and customer success to engineering, data science and research
• Build and scale ML infrastructure: Design and maintain scalable, reliable and efficient production pipelines for feature engineering, training, prediction and model serving using tools including Airflow, Big Query and Kubeflow
• Drive model performance: Train, validate and deploy high-quality ML models, applying advanced techniques in feature selection, hyperparameter tuning and model architecture choices to improve the accuracy of our products
• Accelerate ML development: Optimize feature engineering pipelines for performance and scalability while collaborating with Data Science to research, develop, and deploy new features that improve model accuracy
• Ensure reliability: Implement comprehensive model monitoring, automated training pipelines, and observability solutions to maintain model health and performance
• Champion best practices: Apply CI/CD principles including automated testing, model validation, and deployment strategies