• Help define and refine the software development practices that make our team effective • Write clean, maintainable, and testable code with an eye towards observability and resilience • Coach and empower the less senior engineers on your team to help them level up and own larger epics • Work with cross-functional team members and stakeholders to decide on the vision and scope of your product area • Work with Management, Product Engineering, and Operations to plan and forecast quarterly goals that include a mix of tech debt, bug fixes, and new features • Champion the entire software development lifecycle from requirements and design to testing, deployment, and production support/monitoring • Take on new adventures across the full-stack as we continue to push Cribl Cloud forward • Share on-call and take part in team that embraces a model of service ownership • This position will require stand-by, on-call, or off-hours duties

• 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