• Identifying high-leverage opportunities in incentive optimization, defining the technical roadmap, and driving end-to-end execution of ML/AI solutions to deliver measurable business impact. • Partnering closely with product managers, marketers, data scientists, and backend engineers to deeply understand business needs and build impactful ML applications. • Leading and mentoring the team, raising technical standards, and improving team execution efficiency.

• Contribute to the design, development, and deployment of robust machine learning models for production use in real-world security applications. • Develop within the full machine learning lifecycle; from problem definition to data pipeline design, model development, validation, deployment, and monitoring. • Establish and refine best practices in our ML system architecture, CI/CD pipelines for ML, and reproducible research methodologies. • Collaborate with cross-functional stakeholders including product managers, data engineers, and MLOps teams to ensure seamless model integration and delivery. • Perform advanced exploratory data analysis on large-scale sensory datasets (image, audio, radar, accelerometer) to derive insights and guide modeling strategies. • Stay ahead of industry advancements in machine learning, AI sensing, and signal processing, incorporating the latest innovations into Canopy’s technology stack. • Mentor and guide junior engineers and contribute to the hiring process and technical reviews.

• Train, fine-tune, and evaluate machine learning models on real-world workflow and behavioral data • Build predictive models for task outcomes, productivity trends, capacity forecasting, and workflow optimization • Fine-tune large models and foundation models for domain-specific prediction, classification, and embedding tasks • Design and maintain feature pipelines, training loops, and evaluation frameworks • Work with engineers and product teams to integrate trained models into production systems • Monitor model performance and iterate using offline evaluation and live data feedback

• Develop software applications with a strong focus on machine learning • Train deep learning models using PyTorch and Transformers and experiment with (new) techniques to reduce their memory footprint, speed them up, or increase their accuracy • Deploy software applications, including deep learning models, in production, using AWS and Greenhouse’s internal tools • Partner with other members of the R&D team to uplevel their comfort and familiarity with shipping Machine Learning features • Help set vision and strategy for AI within our product suite • Develop applications that are compliant with our AI policies that prioritize privacy, security, ethical concerns, and best practices while handling data • Contribute to and build all phases of the AI system lifecycle from ideation, model development, testing, evaluation, improvements and monitoring