Staff Software Engineer (Performance Advisor)

Join our team as a Staff Software Engineer, where you will provide technical leadership for our Forecasting and Recommendations platforms. You will design, build, and operate high-throughput, low-latency distributed systems that power forecasting, recommendations, and AI-driven decision-making at scale. You will work deeply in backend systems, infrastructure, and AI application architecture, while remaining accountable for reliability, observability, and operational excellence.

• Design, build, and operate systems supporting forecasting, recommendations, and agentic AI workflows in production.
• Write production-quality code daily; own services end-to-end from design through on-call and incident resolution.
• Champion observability and reliability: metrics, logging, tracing, alerting, and post-incident analysis.

• We are looking for an engineer who can contribute immediately, has shouldered real production incidents, and brings strong judgment around building stable, observable, and scalable systems, including modern agentic and LLM-powered applications
• 2+ years of experience in Python and proficiency in one or more backend languages commonly used for large-scale systems (e.g., Python, Java, Go, Scala)
• Expert-level experience with AWS, including designing, deploying, and operating large-scale cloud-native systems
• Bachelor’s or master’s degree in computer science, Mathematics, or a related field, or equivalent practical experience
• 12+ years of professional software engineering experience building and operating production-grade distributed systems
• Strong hands-on experience with Kubernetes, containerized microservices, and modern CI/CD pipelines
• Experience operating software in both on-prem data center and AWS cloud environments
• Fluency with modern AI-assisted development tools (e.g., Cursor, GitHub Copilot) and comfort working in “vibe coding”–style workflows that favor fast iteration, tight feedback loops, and continuous refactoring
• Deep expertise in systems design, including service boundaries, concurrency, data modeling, failure handling, and scalability tradeoffs
• A strong track record of hands-on ownership of business-critical services, including measurable improvements in latency, throughput, stability, or cost
• Production experience supporting machine learning–driven systems (forecasting, recommendations, or similar), with emphasis on serving, pipelines, and infrastructure
• Own and deeply understand large portions of the codebase and system architecture
• Simplify complex systems to increase team velocity and reliability
• Influence standards and architectural decisions across teams through credibility and collaboration
• Be a prolific, hands-on contributor who raises the bar through example
• Ship multiple large services, shared libraries, or major infrastructure improvements
• Balance technical, analytical, and product constraints to deliver pragmatic solutions
• Set short- to medium-term (6–12 month) technical direction for your domain