• Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. • Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. • Architect and operate scalable inference systems, balancing latency, cost, and reliability. • Design and maintain data systems for high-quality synthetic and real-world training data. • Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. • Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. • Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. • Make pragmatic trade-offs and ship improvements quickly, learning from real usage. • Work under real production constraints: latency, cost, reliability, and safety

• Build and maintain cross-platform desktop applications using Electron • Design responsive and scalable UIs for real-time collaboration • Implement desktop-specific functionality including file system access, native notifications, auto-updates, and deep linking • Integrate Artificial Intelligence (AI)-powered features (chat, agents, Artificial Intelligence (AI) assistance) via backend APIs • Optimize startup time, memory usage, and runtime performance • Profile and reduce Electron overhead • Manage large local state and message history efficiently • Ensure smooth real-time updates (messages, typing indicators, presence) • Maintain stability across macOS and Windows environments

• Build core Machine Learning (ML) systems that power a proactive, long-horizon Artificial Intelligence (AI) product. • Own work end-to-end: data preparation, training, evaluation, inference, and iteration. • Turn research ideas into working systems that run reliably in production. • Debug model failures and system issues using real production signals. • Iterate quickly: ship, measure outcomes, refine, and repeat. • Collaborate closely with research, product, and engineering to deliver real user impact. • Mentor and review work from other Machine Learning (ML) engineers through example and technical judgment. • Work under real production constraints: latency, cost, reliability, and safety.

• Lead operational improvement initiatives across the business. • Design scalable operational frameworks. • Improve cross-functional collaboration. • Standardize business processes. • Increase organizational efficiency. • Support strategic execution. • Document workflows. • Improve SOPs. • Build operational playbooks. • Optimize internal processes. • Improve quality assurance. • Reduce operational complexity. • Leverage AI to improve: - Documentation - Meeting management - Workflow design - Knowledge management - Reporting - Process mapping - Decision support - Operational analytics - Communication - Continuous improvement • Collaborate closely with: - Executive Leadership - HR - Product - Marketing - Sales - Partnerships - Customer Success - Engineering - Automation - Finance to ensure operational excellence across the organization. • Monitor KPIs. • Identify bottlenecks. • Improve operational visibility. • Support resource planning. • Drive accountability. • Continuously improve business performance. • Build better systems. • Improve documentation. • Increase automation. • Strengthen AI adoption. • Reduce manual work. • Scale operational excellence.