Machine Learning Engineer

About the role

E-commerce got real-time data infrastructure decades ago. Physical stores still have not. RADAR is changing that. RADAR is building the data infrastructure layer for the physical world, starting with retail. Our hardware-enabled SaaS platform uses proprietary overhead sensors, software, and AI-powered analytics to locate every product in a store, continuously, down to the fixture. We are deployed across 1,400+ stores with retailers including American Eagle Outfitters and Old Navy, processing tens of billions of real-world events every day, delivering 99%+ accuracy in complex, noisy environments - at fleet scale. RADAR is one of the best-funded companies in retail technology, backed by a recent Series B financing at a $1 billion valuation. Inventory accuracy is only the beginning. We believe RADAR can become foundational infrastructure for the physical economy, powering new AI-driven commerce experiences across retail and beyond. Join us if you want to work on a large, unsolved, technically challenging problem with an ambitious team building category-defining technology. OUR VALUES Mission-Driven: We're transforming retail with cutting-edge technology and building something that truly matters. Collaborative Team: We thrive on curiosity, shared goals, and solving complex problems together. High Impact: You'll make meaningful contributions from day one and help shape the future of our product and company. Clear Communication: We value honesty, humility, and respectful dialogue-everyone's voice matters. Balanced Lives: We work hard, but not at the expense of well-being. We respect time, boundaries, and life outside of work. Diverse Perspectives: We believe better ideas come from diverse backgrounds, experiences, and viewpoints. Empathy-Driven Design: We build with deep respect for our end users, listening closely to their feedback and needs. 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. This is a hybrid role based in our Sunnyvale, CA location with a flexible hybrid work schedule of 2-3 days in the office.

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
• ABOUT YOU
• Required :
• 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.)
• Research has shown that women & underrepresented minorities are more likely to read lists of requirements and consider t

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