Machine Learning Engineer

At RADAR, we're transforming the way the world thinks about physical retail. RADAR has raised over $104M from top investors, retailers, and strategics and works with some of the world's top billion-dollar global retailers. We’re building the future of in-store experience where every product and every person can be precisely located in real time.

Our platform combines RFID and AI to unlock hyper-accurate product visibility and automation at scale. From real-time inventory tracking to seamless checkout experiences, our technology empowers some of the world’s largest retailers to streamline operations, reduce loss, and elevate both employee and customer experiences.

We’re a fast-growing, mission-driven startup where bold ideas, collaboration, and impact are at the core of everything we do. Join us as we reshape the physical world with digital precision, starting with retail and expanding far beyond!

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.

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

• 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

• 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 $120,000.00 - $190,000.00. 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.

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.