> Markdown version of [/jobs/ext/1897053-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/1897053-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Engineer

- **Company:** Radar, Inc.
- **Location:** Sunnyvale, CA, United States
- **Experience:** Expert
- **Salary:** $195,000.0 - $264,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Automation of Tests, Microsoft Azure, Big Data, BigQuery, Software as a Service, Cloud Computing, Configuration Management, Continuous Delivery, Continuous Integration, Distributed Systems, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, SQL Databases, Data Streaming, Workflow Management Systems, Data Processing, Scripting, 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:** July 31, 2026
- **Apply:** https://www.careerbuilder.com/job-details/machine-learning-engineer-sunnyvale-ca--9018f8a8-756b-4325-bb51-2c01003eafee

## About the Role

* 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

Preferred:

 * 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.), Amazon Web Services (AWS), Apache Spark, Artificial Intelligence (AI), Best Practices, Big Data, Category Development, Cloud Computing, Communication Skills, Computer Science, Continuous Deployment/Delivery, Continuous Integration, Data Processing, Data Science, Distributed Computing, Diversity, Git, Health Maintenance, Leading Edge Technology, Machine Learning, Microsoft Windows Azure, Model Validation, Performance Modeling, Predictive Modeling, Problem Solving Skills, Production Systems, Python Programming/Scripting Language, Retail, SQL (Structured Query Language), Scientific Research, Software as a Service (SaaS), Source Code/Configuration Management (SCM), Statistics, Team Building, Team Player, Test Automation, Vehicle Fleets, eCommerce

## 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


## Related Videos

- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)
- [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94)
- [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment)
- [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning)
- [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews)

## Related Articles

- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)