Staff Engineer, Revenue Management & Intelligence

Salesloft Salesloft · Enterprise · United States · Engineering

Staff Engineer role focused on building LLM-powered and agentic systems for enterprise revenue management, requiring expertise in distributed systems, cloud infrastructure, and AI tooling. The role involves shaping interfaces, developing new features, debugging production issues, and improving AI tooling adoption.

What you'd actually do

  1. Shape future-proof interfaces that are easy to build against and meet the requirements of client-facing teams
  2. Brainstorm with Product Managers, Designers, Frontend Engineers, and other cross-functional partners to conceptualize and build new features for our growing user base
  3. Produce high-quality results by leading or contributing heavily to large cross-functional projects that have a significant impact on the business
  4. Debug production issues across services and multiple levels of the stack, and uplevel the observability and reliability of the overall system
  5. Improve engineering standards, and AI tooling adoption.

Skills

Required

  • 8+ years of experience in building distributed systems or platform infrastructure designed for high-scale, data-intensive workloads
  • Expert-level proficiency in Java or a similar object-oriented language
  • familiarity with infrastructure tooling (Airflow, Kubernetes, Ray, Terraform)
  • familiarity with service frameworks (FastAPI, gRPC)
  • familiarity with observability platforms (Prometheus, Grafana, DataDog, Splunk)
  • Demonstrated experience working with cloud infrastructure, ideally AWS or GCP, including Kubernetes clusters (GKE/EKS), serverless architectures, and managed services (e.g., Lambda, Cloud Run, ECS)
  • Proven experience with distributed data infrastructure: message queues (Kafka, Kinesis), caching systems (Redis, Memcached), database optimization, and building resilient, fault-tolerant architectures
  • Familiarity with SQL (postgres) / NoSQL (MongoDB) and data warehouse modeling
  • Solid understanding of platform monitoring and optimization, including identifying performance bottlenecks, latency optimization, cost management, and scaling high-throughput API services efficiently
  • Deep experience building LLM-powered and agentic systems beyond chat-based interfaces

Nice to have

  • AI tooling adoption

What the JD emphasized

  • AI-driven enterprise-grade applications
  • Deep experience building LLM-powered and agentic systems beyond chat-based interfaces

Other signals

  • building enterprise-grade AI-driven applications
  • improve engineering standards, and AI tooling adoption
  • Deep experience building LLM-powered and agentic systems beyond chat-based interfaces