• Support teams of developers to resolve bugs and workarounds for our most critical and impactful products. • Work with our team of Artificial Intelligence experts to enhance our products • Work in a fast-paced agile software development environment • Develop applications that interface with multiple systems (web, desktop, telephony systems, etc.) • Interact with product stakeholders to show off your work and gain feedback • Bring your ideas and try new things • Perform other duties as required

• Help build the leading platform for machine learning & AI in the enterprise • Design, code, and implement elegant, scalable, enterprise-quality application services • Work to enhance developer velocity and team agility • Build strong relationships and collaborate with platform and UI engineers, quality engineers, UX designers, as well as Product Management, Field Engineering, and other external partners

• At New Relic, we provide businesses with a state-of-the-art observability platform, leveraging advanced technologies to deliver real-time insights into the performance of software applications and infrastructure. • Our platform delivers deep insights into application performance, enabling businesses to make data-driven decisions with confidence. • As an industry innovator, New Relic is committed to harnessing the power of artificial intelligence and machine learning to enhance observability and predictive analytics while ensuring the integrity and security of our solutions. • Our team values creativity, collaboration, and cutting-edge technology, recognizing the vital role AI plays in transforming digital operations. • We are transforming the platform from legacy DevSecOps to Agentic AI Developer Platform (powers all of engineering). • We aim to revolutionize our internal tooling with AI to enhance developer productivity, enabling our engineers to build and innovate faster while ensuring robust security and seamless integration throughout the development lifecycle.

• Design model training and inference workflows with clear versioning, lineage, and promotion criteria where models are part of the system. • Define service responsibilities, interfaces, and data contracts that evolve safely. • Specify behavior under retries, timeouts, partial failures, and dependency degradation. • Choose consistency and durability guarantees that match risk, latency targets, and operational realities. • Design the request path for predictable tail latency and controlled resource usage. • Build and operate high-performance services and APIs that keep authentication reliable, secure, and fast at scale. • Implement distributed services that are safe under concurrency and robust to duplicate and out-of-order events. • Build real-time scoring and decision services with clear input/output contracts and bounded execution time. • Build distributed training pipelines that scale, are reproducible, and produce auditable artifacts. • Build pipelines that move data and model artifacts through validation, promotion, and release. • Define automated quality gates for service changes and releases. • Add checks for data quality, schema/contract adherence, and training-serving consistency where appropriate. • Define acceptance criteria tied to measurable outcomes and production behavior. • Coordinate multi-service releases with clear cutover and recovery plans. • Use production signals to validate rollouts and trigger rollback when risk is high. • Own after-hours production releases, including rollout validation, monitoring, and rollback execution. • Instrument the full path with metrics, logs, and traces that enable fast detection and diagnosis. • Implement alerting that reflects user impact, not just component health. • Lead incident response for your services, restore service quickly, and communicate clearly during events. • Drive reliability work through SLIs, SLOs, and error budgets, and make tradeoffs explicit. • Improve performance and cost through profiling, load testing, and capacity planning.