At Rise, we operate at the intersection of extreme scale, real-time decisioning, and high-frequency data. As a cloud-native adtech company, our platform processes massive request volumes where every millisecond of latency and every fraction of a cent in cloud compute directly impacts our bottom line.
Driven by rapid growth, we need engineers who love analyzing system bottlenecks to maximize efficiency—connecting the dots between technical innovation, system performance, and revenue growth.
This is a core Back-End Engineering role. You will build high-performance, ultra-low-latency backend services that execute complex business logic, integrate with Data Science models, and serve billions of real-time requests.
Our Engineering Mindset We move fast, automate aggressively, and relentlessly optimize everything we touch—from network I/O and query costs to workflow automation. We actively integrate AI tools and workflows directly into our daily SDLC to move faster, elevate code quality, automate routine overhead, and accelerate shipping cycles. Our developers operate with a full ownership mindset, communicating across multiple cross-functional stakeholders with a broad, business-oriented perspective. We thrive on a "can-do" culture and take pride in engineering elegant, cost-optimized solutions.
Responsibilities
- Drive Business Impact Through Feature Delivery: Own full-lifecycle feature development from concept to deployment. Translate ambiguous business goals into clear technical execution plans, align with cross-functional stakeholders, and deliver features that directly drive revenue and operational efficiency.
- Engine for High Throughput: Design, build, and optimize low-latency backend microservices using Golang and Rust to handle heavy adtech workloads.
- Leverage AI Across the SDLC: Integrate AI-driven coding assistants and automated tooling into your everyday workflow—from code generation and automated testing to debugging, refactoring, and documentation—to maximize developer velocity.
- Relentlessly Optimize Infrastructure & Costs: Analyze system bottlenecks, conduct CPU/memory profiling, optimize resource utilization, and drive down our GCP compute and data transfer costs without sacrificing reliability.
- Bridge Backend & Data Science: Integrate production-ready Data Science models and complex ad-server business logic into fast, reliable execution paths.
- Manage Cloud-Native Architecture: Deploy, maintain, and scale microservices on GCP using Google Kubernetes Engine (GKE), Docker, and modern CI/CD pipelines.
- Automate Everything: Identify manual processes, operational overhead, or inefficient code paths across our stack and build automated solutions to eliminate them.
Requirements
- 3+ years of hands-on experience in backend engineering, building high-concurrency, low-latency applications (strong proficiency in Golang and/or Rust required, or deep background in Java/C#).
- AI-Augmented Development Mindset: Experience leveraging modern AI developer tools (e.g., Claude Code, Cursor, LLM-driven testing or debugging tools) across the SDLC to ship high-quality code faster.
- High-Scale System Design & Profiling: Proven track record of designing microservices and optimizing applications for performance, throughput, and memory footprint in production using CPU/memory profiling tools.
- Modern Data Store Expertise: Hands-on experience with high-performance and analytical data stores such as Redis, Valkey, Aerospike, BigTable, and BigQuery.
- Cloud-Native Proficiency: Deep familiarity with GCP and container orchestration via Kubernetes (GKE) and Docker.
- Cost & Performance Mindset: You don't just write code that works; you care about how it runs, how much memory and CPU it consumes, and how much it costs to run at scale.
- Full End to End Ownership: Ability to manage initiatives independently, navigate loosely defined tasks, and communicate effectively with cross-functional teams to keep projects moving forward.
- Relentless Curiosity & Autonomy: A pragmatic, "can-do" problem solver who loves learning new tech and taking complete ownership of solutions.
Advantages
- Experience with probabilistic data structures (e.g., Bloom filters, HyperLogLog).
- Familiarity with Lua scripting for in-memory databases.
- Experience with gRPC for high-performance microservice communication.