Backend Engineer
Backend Engineers at AI companies build and operate the server-side systems that AI products and infrastructure run on—distributed services, REST APIs, data pipelines, and the databases behind them. The day-to-day is classical backend work: designing services for reliability and scale, optimizing query performance, instrumenting observability, owning on-call and SLOs, and partnering with product and frontend teams to ship features end-to-end. AI-specific surfaces—high-throughput inference serving paths, telemetry pipelines for GPU-dense infrastructure, agent runtime systems—appear in some of these jobs, particularly at infrastructure and platform companies, but the canonical role is recognizable as backend engineering across any high-growth software business. Backend Engineers typically sit within product, platform, or core services teams, often as the foundational layer that both product engineers and ML engineers build on top of.
Skills
What companies are looking for in this role.
Designing and building scalable backend services and APIs that handle high throughput and low latency requirements
Architecting distributed systems with focus on reliability, performance optimization, and production excellence
Developing cloud-native infrastructure and services on major cloud platforms
Implementing observability solutions including metrics, logging, tracing, and alerting systems
Designing and implementing secure authentication, authorization, and data protection mechanisms
Debugging complex distributed systems and performing root cause analysis on production issues
Designing APIs and integration patterns for connecting enterprise systems and external services
Building and maintaining CI/CD pipelines, testing infrastructure, and deployment automation
Managing relational and non-relational databases including optimization and scaling
Building real-time and streaming data processing systems with backpressure handling
Establishing and enforcing service level objectives, agreements, and reliability standards
Implementing event-driven architecture and stream processing for complex workflows
Managing GPU and heterogeneous compute resources for ML workloads
Implementing compliance and security frameworks including PCI DSS and data protection standards
Building high-throughput video and media processing pipelines
Designing systems for real-time voice and communication processing
Building search and retrieval systems with emphasis on ranking and relevance
Implementing AI model serving and inference optimization in production systems
Building agentic workflow orchestration and automation platforms
Designing systems that integrate large language models into production applications
Collaborating with cross-functional teams including product, data science, and infrastructure
Translating business requirements into technical architecture and system design
Owning end-to-end feature delivery from design through production operation
Mentoring junior engineers and establishing technical standards and best practices
Leading production incident response and driving post-mortem processes
Technology
The tools and technologies that define this role.
Open Jobs
522 open Backend Engineer jobs across 101 companies.
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