Senior Backend Engineer, LLM Inference Systems

Inception; San Francisco (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Inception in San Francisco is seeking experienced backend engineers to own the systems that serve our diffusion LLMs in production. You will build and operate infrastructure that handles billions of inference requests, optimizing for latency, throughput, cost, and reliability.

This role sits at the intersection of ML systems and backend infrastructure, with responsibilities spanning scalable services, model serving, load balancing, canary deployments, and observability tooling to ensure SLA

Qualifications

• BS/MS/PhD in Computer Science or a related field (or equivalent experience).
• 5+ years of experience building production backend systems.
• Strong proficiency in Python, including async programming and concurrent systems.
• Solid understanding of distributed systems, networking, and load balancing at scale.
• Familiarity with Kubernetes, CI/CD pipelines, and cloud infra (AWS and/or Azure).

Responsibilities

• Design, build, and operate scalable backend services and model serving infrastructure for our diffusion LLMs.
• Implement and manage load balancing, autoscaling, and traffic routing for model endpoints.
• Build systems for model versioning, canary deployments, and zero-downtime rollouts.
• Develop monitoring, alerting, and observability tooling to ensure SLA compliance and rapid incident response.
• Benchmark and evaluate serving frameworks and hardware configurations to inform infrastructure decisions.

Skills

Python (async programming)

Distributed systems

Load balancing at scale

Networking fundamentals

Backend software design

Education

BS/MS/PhD in Computer Science or related field

Tools

Kubernetes

CI/CD pipelines

AWS / Azure cloud infra

Terraform

Prometheus

Grafana

vLLM / Triton / TensorRT-LLM

Job description

Inception in San Francisco is seeking experienced backend engineers to own the systems that serve our diffusion LLMs in production. You will build and operate infrastructure that handles billions of inference requests, optimizing for latency, throughput, cost, and reliability.

This role sits at the intersection of ML systems and backend infrastructure, with responsibilities spanning scalable services, model serving, load balancing, canary deployments, and observability tooling to ensure SLA