AI SDE 3 Backend - Hybrid

About Us

Hiver offers teams the simplest way to offer outstanding and personalized customer service. As a customer service solution built on Gmail, Hiver is intuitive, super easy to learn, and delightful to use. Hiver is used by thousands of teams at some of the best-known companies in the world to provide attentive, empathetic, and human service to their customers at scale. We’re a top-rated product on G2 and rank very highly on customer satisfaction.

At Hiver, we obsess about being world-class at everything we do. Our product is loved by our customers, our content engages a very wide audience, our customer service is one of the highest rated in the industry, and our sales team is as driven about doing right by our customers as they are by hitting their numbers. We’re profitably run and are backed by notable investors. K1 Capital led our most recent round of $27 million. Before that, we raised from Kalaari Capital, Kae Capital, and Citrix Startup Accelerator.

Opportunity



As Software Development Engineer III at Hiver, you will build and scale backend systems that power both traditional workflows and AI-driven features. We process 5M+ emails daily for thousands of active users.

You Will

• Own low-latency, cost-efficient AI endpoints and data pipelines.
• Integrate intelligent workflows into existing services with measurable impact.
• Mentor engineers and raise engineering standards.
  
  

What you will be working on?

• Make the architecture scalable for growing traffic and AI workloads.
• Build frameworks to monitor, improve, and optimize AI-backed systems.
• Improve reliability, latency, and performance of both traditional and AI services.
• Design and maintain APIs in monolith and microservices environments.
• Build event-driven systems with Kafka/RabbitMQ for high-volume pipelines.
• Implement AI components: model serving, inference/generation APIs, retrieval/RAG, embeddings, rerankers, vector stores.
• Stand up evaluation + guardrails: test sets, canaries, A/B, drift detection, content safety, fallback chains.
• Build secure storage and processing for large-scale structured/unstructured data; enforce data contracts.
• Own observability: tracing, metrics, feature flags, model/version routing, SLOs, error budgets.
• Debug production issues across services and layers; lead incident response and postmortems.
• Collaborate with AI/ML engineers and data scientists to productionize models and notebooks.
• Optimize cost/latency via caching, token budgets, autoscaling, and hardware placement.
  
  

What we are looking for?

• Strong experience scaling backend/distributed systems and microservices.
• Concurrency expertise; deep understanding of reliability, performance, and resiliency.
• Event-driven architecture with Kafka/RabbitMQ; high-volume data pipelines.
• Hands-on SQL; working knowledge of NoSQL/caches; exposure to vector databases preferred.
• Production model-serving exposure: embeddings, RAG, realtime inference APIs, or eval harnesses.
• Solid in one or more of Go/Java/Python; high-quality, maintainable code.
• Cloud deployment (AWS preferred; GCP/Azure ok). Containers, CI/CD, infra as code.
• Security and privacy fundamentals for PII and prompt/content safety.
• Nice to have: Triton/TorchServe/vLLM, quantization, OpenTelemetry, pgvector/OpenSearch/Pinecone, feature-flag platforms.
• Prior collaboration with AI/ML teams from research to production is desirable.
• Track record of scaling systems for 5+ years; ownership of production services.