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# Machine Learning Engineer

- **Company:** Radar, Inc.
- **Location:** Sunnyvale, CA, United States
- **Experience:** Expert
- **Salary:** $140,000.0 - $220,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Automation of Tests, Big Data, BigQuery, Continuous Integration, Distributed Systems, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, Data Streaming, Workflow Management Systems, Feature Engineering, Sql Optimization, Pytorch, Apache Spark, Model Validation, Git, Scikit Learn, Kubernetes, Information Technology, Apache Flink, Deployment Automation, Xgboost, Dask, Apache Kafka, Feature Selection, Machine Learning Operations, Software Version Control
- **Published:** May 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=f34740da82b33461

## About the Role

Do you have experience in Version control systems?, Do you have a Bachelor's degree?, * 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, * 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.)

## Description

* 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


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