Applied AI Scientist (Hybrid)

📋 Description

• • Drive advancements in LLMs and applied AI systems through rapid experimentation and iteration in a fast-evolving AI landscape
• • Leverage, fine-tune, and deploy machine learning models, owning the full lifecycle from experimentation to production, scaling, and monitoring
• • Build AI-powered product features that solve critical business problems across team management domains such as scheduling, communication, and payroll
• • Develop and track metrics that measure real customer impact, ensuring AI solutions are aligned with tangible business outcomes
• • Partner cross-functionally with Product, Engineering, and Data teams to deliver production-ready AI solutions with clear documentation and reproducibility
• • Communicate technical insights, experiment results, and model performance clearly to both technical and non-technical stakeholders
• • Support leadership and teams with data-driven decision-making by translating AI outputs into actionable business recommendations
• • Continuously improve internal workflows using AI tools and automation to increase team velocity and operational efficiency
• • Evaluate models using a combination of technical metrics (e.g., accuracy, latency, recall) and business impact indicators (e.g., user retention, time savings)
• • Break down complex, ambiguous problems into structured, solvable components with clear hypotheses and validation paths
• • Maintain and evolve AI systems in production by monitoring performance, identifying degradation, and implementing retraining pipelines
• • Contribute to the development of AI fluency across the organization by sharing knowledge, best practices, and tooling insights
• • Ensure all AI implementations are ethically grounded, scalable, and compliant with company data policies and privacy standards
• • Collaborate with design and UX teams to integrate AI features into intuitive user experiences that enhance productivity for small business teams
• • Actively participate in sprint planning, technical reviews, and retrospectives to refine AI delivery processes
• • Stay current with emerging AI research and industry trends to evaluate relevance for Homebase’s product roadmap
• • Use Python and SQL to manipulate data, prototype models, and automate data pipelines for training and evaluation
• • Document model architecture, training data sources, inference logic, and performance benchmarks for auditability and team knowledge sharing
• • Engage in customer-centric design by analyzing real user behavior data to inform AI feature priorities and improvements
• • Advocate for responsible AI usage by identifying and mitigating bias, fairness concerns, and unintended consequences in deployed systems
• • Participate in customer-facing events like Customer Days to observe how AI features are used in real-world small business environments
• • Contribute to the definition of success metrics and KPIs for AI initiatives in alignment with product and company goals
• • Proactively identify opportunities to apply AI to under-served or manual processes within Homebase’s operational ecosystem
• • Maintain agility and adaptability in response to rapid changes in AI capabilities, customer needs, and product direction