• Design, build, and release AI products/features that solve real user problems • Design and implement streaming/batch data pipelines to support training and inference • Write production-ready code and move it to production with the help of cloud services and CI/CD techniques • Continuously monitor and improve the quality and performance of the systems • Learn by doing — while owning real problems and demonstrating end-to-end ownership throughout the system • Build resilient, performant pipelines and services in production • Own system architecture, performance, observability, and scalability • Collaborate across teams to turn ideas into impactful products • Mentor engineers and help set technical direction

• Design, build, and release AI products/features that solve real user problems • Design and implement streaming/batch data pipelines to support training and inference • Write production-ready code and move it to production with the help of cloud services and CI/CD techniques • Continuously monitor and improve the quality and performance of the systems • Learn by doing—while owning real problems and demonstrating end-to-end ownership through the whole system

• Innovate: Stay at the forefront of AI/ML advancements, continuously exploring new techniques, models, and tools. Your insights will help drive innovation and keep our platform at the cutting edge of technology. • Collaborate: Work closely with core platform and automation engineers, as well as other stakeholders, to identify and address business needs through Machine Learning solutions. You will play a key role in bridging the gap between AI research and practical application. • Integrate: Utilize APIs, pre-trained models, and other tools to implement AI-driven features across our platform. You will focus on integrating these capabilities into production systems, enhancing the functionality and performance of our solutions. • Optimize: Fine-tune and optimize existing solutions for specific product requirements, ensuring that AI integrations are efficient, scalable, and reliable.

• Build, train, and refine machine learning and deep learning models using time-series, sensor, and behavioral data. • Integrate data from wearables, fitness tracking platforms, and device APIs to create a clear story from movement, patterns, and activity signals. • Develop and maintain data pipelines that support both batch and real-time analytics. • Own model deployment in production environments — your models won’t live in notebooks; they’ll live in the world. • Work closely with engineering teams to integrate ML models into mobile and web apps. • Support logic for fraud, spoofing, and anomaly detection, ensuring data reflects real human activity. • Make complex outputs easy to understand — not just for engineers, but for product and business users too.