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Sunnyvale, California, United States
Hybrid
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.
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.
At RADAR, your base pay is one part of your total compensation package. The expected base salary range for this position is $195,000 - $264,000. 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.