Backend Engineer

Role Overview

We're seeking a Backend Engineer to build and scale our edge inference infrastructure. You'll architect distributed compute systems handling GPU-accelerated AI workloads across edge nodes with sub-10ms latency requirements.

Core Responsibilities

• Infrastructure Engineering
• Design and implement Kubernetes-native distributed compute platforms
• Build GPU resource management and allocation systems
• Develop edge deployment pipelines with automated testing
• Create high-performance inference serving infrastructure

Backend Systems

• Architect microservices for distributed model serving
• Implement API gateways with OpenAI and Hugging Face-compatible endpoints
• Build dynamic resource allocation and load balancing
• Design multi-backend systems with mutual exclusivity enforcement

Performance & Optimization

• Optimize GPU memory utilization and inference latency
• Implement streaming inference with TensorRT acceleration
• Build comprehensive monitoring and observability systems
• Design automatic scaling based on workload patterns

Required Technical Skills

Core Infrastructure

Kubernetes: Production experience with cluster management, resource allocation, networking

Containerization: Docker, container security, multi-stage builds, optimization

Distributed Systems: Service mesh, load balancing, distributed consensus, fault tolerance

Cloud: GitOps, infrastructure as code, AWS, CDK

Backend Development

Languages: TypeScript, Go, Python, or Rust

APIs: RESTful services, gRPC, WebSocket streaming, rate limiting

Databases: Distributed databases, caching systems, data consistency

Message Queues: Kafka, Redis, SQS, distributed event systems

AI Inference Infrastructure

GPU Computing: NVIDIA CUDA, TensorRT, GPU memory management

AI/ML Serving: Triton Inference Server, model optimization, batch processing

Performance: Latency optimization, throughput tuning, resource profiling

Preferred Experience

Infrastructure Platforms

• Edge computing deployments
• Multi-region distributed systems
• Hardware acceleration (GPUs)
• Container security (Kata, gVisor)

Monitoring & Operations

• Prometheus, Grafana, distributed tracing
• SRE practices, incident response
• Capacity planning, cost optimization
• Automated testing and deployment

What You'll Build

Edge Inference Platform

• Multi-tenant GPU inference clusters serving 10,000+ concurrent requests
• Sub-10ms latency requirements with geographic distribution
• Automatic model loading and resource optimization
• Comprehensive health monitoring and alerting

Backend Architecture

• Microservices handling model lifecycle management
• API gateway with authentication and rate limiting
• Dynamic backend switching (Python/TensorRT-LLM)
• Streaming inference with WebSocket support

DevOps Infrastructure

• Kubernetes operators for inference workload management
• Automated testing covering performance and reliability
• GitOps deployment with rollback capabilities
• Cloud and edge resource monitoring and cost optimization