• Hire, mentor and guide a dynamic team of highly skilled machine learning engineers, fostering their technical growth and professional development. • Proactively take initiatives to drive for outcomes over the scope of the team. • Build relationships and drive alignment with stakeholders within Community Support and across external teams. • Communicate difficult or complex concepts and decisions clearly and effectively with multiple stakeholders across various functions. • Provides critical guidance and input in prioritization over multiple major projects. • Foster rapid development cycles without sacrificing quality, collaborating closely with platform, backend, and frontend engineers to engineer robust ML models and systems that enhance community support initiatives. • Uphold Technical Standards: Conduct thorough design and architecture reviews to continually elevate our standards of technical excellence.

• Contribute to challenging projects • Drive a project from end-to-end that aligns with interests and goals • Collaborate with team members to achieve milestones • Communicate with stakeholders to provide project updates • Participate in the Engineering org and broader Airbnb community

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role involves working with BILL’s AI Product and Platform team to build the intelligence backbone that powers every AI and agent-driven experience across our products. - Architect large-scale LLM infrastructure, evaluation, and data systems. - Partner closely with product and engineering teams to launch new agents. - Drive down latency and cost while setting technical standards for AI across the company. - Lead the architectural strategy and design for end-to-end ML systems. - Own the operational roadmap for production-level inference, fine-tuning, and LLM orchestration at a large scale. - Develop and standardize reusable ML modeling frameworks and high-performance data pipelines. - Lead applied research initiatives and design experiments to push the boundaries of BILL's AI capabilities. - Oversee the evolution of BILL's data infrastructure for maximum scalability and long-term maintainability. - Identify emerging AI opportunities and transform them into core business goals. - Define and enforce best practices for ML engineering and mentor junior engineers. Qualifications - Proven leadership in leading complex, high-impact AI projects from ideation to production. - Deep expertise in building backend systems that support AI-driven products. - Expert-level experience in building complex data pipelines (ETL, cleaning, structuring). - Ability to bridge the gap between theoretical ML research and production engineering. - Strong communication skills with a history of shaping team goals and defining engineering standards. - Experience managing risks and trade-offs of complex AI deployments. Requirements - This position is eligible for visa sponsorship. - The estimated salary range for this role in San Jose is $216,000 — $269,900 USD. Benefits - 100% paid employee health, dental, and vision plans (choose HMO, PPO, or HDHP). - HSA & FSA accounts. - Life Insurance, Long & Short-term disability coverage. - Employee Assistance Program (EAP). - 11+ Observed holidays and wellness days and flexible time off. - Employee Stock Purchase Program with employee discounts. - Wellness & Fitness initiatives. - Employee recognition and referral programs. - And much more.

• Design and implement scalable MLOps/LLMOps platforms supporting the full ML lifecycle: data versioning, model training, evaluation, deployment, and monitoring • Build and maintain CI/CD pipelines for ML models and LLM applications with automated testing, validation, and rollback capabilities • Develop infrastructure-as-code (IaC) for reproducible, version-controlled ML environments • Architect model serving infrastructure with auto-scaling, A/B testing, and canary deployment capabilities • Build platforms for LLM fine-tuning, prompt management, and experimentation at scale • Implement evaluation frameworks for LLM performance, quality, safety, and cost optimization • Design and deploy enterprise-grade AI agents and copilots with robust monitoring and guardrails • Establish LLM observability: token usage tracking, latency monitoring, prompt/response logging, and cost attribution • Own uptime, reliability, and performance of ML/LLM services (SLIs/SLOs) • Implement comprehensive monitoring, alerting, and incident response for ML systems • Participate in on-call rotations and drive post-incident reviews to improve system resilience • Build automation and tooling to reduce toil and accelerate ML development velocity • Partner with ML Engineers and Data Scientists to translate research into production-ready systems • Collaborate with platform and infrastructure teams on cloud architecture and resource optimization • Mentor team members on MLOps best practices, production ML patterns, and operational excellence • Drive technical decisions on tooling, frameworks, and architectural patterns