ABOUT THE JOBWe 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 YOURequired:- 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.)
At RADAR, your base pay is one part of your total compensation package. The expected base salary range for this position is $140,000 - $220,000. Individual pay is determined by work location and additional factors, including job-related skills, experience and relevant education or training.You will also be eligible to receive other benefits including: equity, comprehensive medical and dental coverage, life and disability benefits, 401k plan, flexible time off, and paid parental leave. The pay range listed for this position is a good faith and reasonable estimate of the range of possible base compensation at the time of posting.
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.
Use of artificial intelligence or a LLM such as ChatGPT during the interview process will be grounds for rejection of your application process.
