Software Engineer; Backend-Focused; Posted ago

Software Engineer, Backend / AI Infrastructure

Location:

Remote

Compensation: $140,000–$225,000 + equity + bonus

Fuel Talent is partnering with a growing AI company to hire a Senior/Staff Software Engineer focused on backend and AI infrastructure.

This is a high-impact individual contributor role working on the backend systems that support production AI applications and infrastructure. You'll build distributed services, APIs, model-facing platform capabilities, and infrastructure designed to operate reliably as usage scales.

The ideal engineer brings strong backend fundamentals and is excited about applying that experience to the unique infrastructure challenges created by modern AI systems.

• Build and maintain backend services supporting production AI and LLM workloads, including routing, rate limiting, authentication, observability, and resource management.
• Design scalable APIs and distributed systems that sit between applications and underlying AI infrastructure.
• Contribute to secure execution and isolation capabilities for AI-powered and agentic applications.
• Build and support containerized platform services and autoscaling infrastructure.
• Write high-performance backend code using Go, Rust, or asynchronous Python.
• Work with technologies such as Kubernetes, gRPC, Envoy, FastAPI, and Open Telemetry.
• Improve system reliability, latency, performance, and cost visibility as the platform scales.
• Collaborate across backend, platform, infrastructure, and security initiatives as technical priorities evolve.
• Help establish technical direction and engineering best practices as an early member of the engineering organization.

• 4+ years of professional software engineering experience with a strong backend focus.
• Strong understanding of distributed systems, API design, concurrency, and production backend architecture.
• Production experience with Go, Rust, or asynchronous Python.
• Experience building and operating scalable backend services in production.
• Familiarity with Kubernetes, containers, and cloud infrastructure.
• Ability to work across multiple areas of a platform rather than being limited to one narrow technical specialty.
• Strong engineering judgment and comfort making architectural decisions in ambiguous or rapidly evolving environments.
• Interest in building infrastructure for modern AI and LLM-powered applications.

Any of the following would be valuable, but aren't required:

• Experience building infrastructure for LLMs, AI applications, or model-serving systems
• Rate limiting, caching, request routing, or usage/accounting systems
• Kubernetes operators and autoscaling
• Secure execution, sandboxing, or workload isolation
• Envoy, gRPC, or similar networking technologies
• Open Telemetry and production observability
• GPU or model-serving infrastructure
• CI/CD, Docker, Terraform, Pulumi, or similar infrastructure tooling
• Vector or graph databases
• PyTorch, Tensor Flow, JAX, ONNX, or related ML technologies
• Experience working in both startup and larger enterprise environments

You're a strong backend engineer who enjoys solving infrastructure problems where performance, reliability, and scalability matter. You don't need to be an ML researcher, but you're interested in the engineering challenges that come with putting AI systems into production.

You're comfortable taking ownership, navigating ambiguity, and working across different parts of the stack as priorities evolve. You enjoy environments where engineers have meaningful influence over architecture and technical direction.

This is an opportunity to work on technically challenging AI infrastructure while maintaining significant ownership over the systems you build.