• Take end-to-end ownership of new features, working with others in the Nexus and OSS teams to deliver exceptional reliability and a great developer experience. • Design and build Nexus interfaces for the Temporal SDKs used by customers to work with Temporal in their programming language of choice. • Tightly integrate Temporal SDKs with their respective languages, making best use of type systems and language runtimes, to execute application code in a durable, deterministic way. • Develop features that provide a foundation for the reliable execution of the current wave of agentic AI systems. • Work directly with our community to debug issues that need expert attention, and get feedback on Temporal SDK features and APIs. • Write publicly-readable technical documentation describing Temporal concepts and APIs. • Travel to meet your coworkers for a week once or twice a year.

• Architect and implement complex AI/ML systems that align with business and product objectives within the AWS cloud. • Serve as a subject matter expert on application engineering systems integrating with AI/ML components. • Drive large scale AI/ML project execution and foster cross functional collaboration between data science, ML engineering, and product teams. • Define and champion best practices for engineering, including code quality, system reliability, and operational excellence. • Mentor ML engineers to elevate team capabilities and maintain technical excellence.

• Own the end-to-end architecture for MLS and property data: streaming and batch pipelines, microservices, storage layers, and APIs • Design and evolve event-driven, Kafka-based data flows that power listing ingestion, enrichment, recommendations, and AI use cases • Drive technical design reviews, set engineering best practices, and make high-quality tradeoffs around reliability, performance, and cost • Design, build, and operate backend services (Python or Java) that expose listing, property, and recommendation data via robust APIs and microservices • Implement scalable data processing with Spark or Flink on EMR (or similar), orchestrated via Airflow and running on Kubernetes where applicable • Champion observability (metrics, tracing, logging) and operational excellence (alerting, runbooks, SLOs, on-call participation) for data and backend services • Build and maintain high-volume, schema-evolving streaming and batch pipelines that ingest and normalize MLS and third-party data • Ensure data quality, lineage, and governance are built into the platform from the start—supporting analytics, AI/ML, and customer-facing features • Partner with analytics engineering and data science to make data discoverable and usable (e.g., semantic layers, documentation, self-service tooling) • Collaborate with ML/AI engineers to design and scale AI agents that automate MLS feed onboarding, listing discrepancy triage, and other operational workflows • Work with frameworks such as PydanticAI, LangChain, or similar to integrate LLM-based agents into our data and service architecture • Help define and implement evaluation, logging, and feedback loops so these agents and data-driven products continuously improve • Collaborate closely with Product, Engineering, and Operations to shape the roadmap for our data platform, MLS capabilities, and AI-powered experiences • Translate ambiguous business and customer problems into clear technical strategies and phased delivery plans • Mentor and unblock other engineers; elevate the overall level of technical decision-making on the team via pairing, reviews, and design guidance

• Build and evolve an internal developer platform with clear self-service workflows ("golden paths") that makes it easy for Clever engineers to build, deploy, and scale well-architected applications. • Define and maintain standards, templates, and automation that help teams ship quickly and safely without sacrificing reliability, security, or compliance. • Collaborate with product engineering teams to understand friction points, gather requirements, and iterate on platform capabilities as an internal product. • Provide observability primitives (logging, metrics, tracing, alerting) and opinionated defaults so teams can operate services effectively with minimal toil. • Implement multi-region capabilities, including repeatable environment provisioning, resiliency patterns, and fast region failover. • Lead resiliency initiatives and implement reliability engineering practices to meet or exceed 99.98% uptime for critical services. • Model and reinforce Clever's engineering culture and practices, including collaboration, operational excellence, and continuous improvement.