Machine Learning Engineer

RADAR•San Diego, CA

69d•$120,000 - $190,000

About The Position

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.

Requirements

2+ 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

Nice To Haves

Experience with real-time streaming data (Kafka, Flink, Pub/Sub.)
Bachelor's degree in Computer Science, Statistics, or related field
Experience with MLOps tools (MLflow, Weights & Biases, etc.)

Responsibilities

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
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
Champion best practices: Apply CI/CD principles including automated testing, model validation, and deployment strategies