Cloud Inference Launch Engineer: Scale & Optimize LLM Infra

United States Digital Space LLC; Washington; On-site; USD 320,000 - 485,000; Full time

Job summary

United States Digital Space LLC is seeking an engineer to join the Cloud Inference team and scale Claude across AWS, GCP, Azure, and future CSPs. You will own end-to-end inference on each cloud platform, from API integration to deployment and daily operations.

You will focus on fast, cost-efficient validation, performance improvements, and reliability to ensure consistent behavior across providers and accelerate model delivery.

Qualifications

• Have a strong interest in LLM serving; prior inference or ML experience is not required
• Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users
• Have a track record of building automation or test infrastructure that measurably improved release velocity or reliability
• Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code, or container orchestration
• Thrive in cross-functional collaboration with both internal teams and external partners
• Are a fast learner who can quickly ramp up on new technologies, hardware platforms and provider ecosystems
• Are highly autonomous and take ownership of problems end-to-end, including work that falls outside your job description

Responsibilities

• Be on the critical path for frontier model launches, bringing up inference for new model architectures and shipping them to cloud platforms in lockstep with our first-party platform
• Work with the core inference team to bring new inference features (e.g. structured sampling, prompt caching, and more) to cloud platforms, owning the platform-specific integration that gets them to production
• Identify and dive deep on the gaps that make inference behave differently across first-party and CSPs — config drift, observability, deployment patterns, hard cross-platform bugs — and fix them at the source rather than building platform-specific workarounds
• Design, build, and own the CI/CD infrastructure for the inference server and load balancer across cloud platforms, with shadow traffic, performance baselines (throughput and latency), and correctness checks that catch regressions before production
• Drive down merge-to-production cycle time by making validation faster, more parallel, and cost-effective enough to run on the same constrained accelerator pool that serves customers, without trading away reliability
• Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads

Skills

Distributed systems

High-performance software

Automation / test infra

Cloud platforms AWS/GCP/Azure

Kubernetes exposure

Cross-functional collaboration

Fast learner

Autonomous ownership

Education

Bachelor's degree or equivalent

Tools

Kubernetes

Infrastructure as Code

Container orchestration

Job description

United States Digital Space LLC is seeking an engineer to join the Cloud Inference team and scale Claude across AWS, GCP, Azure, and future CSPs. You will own end-to-end inference on each cloud platform, from API integration to deployment and daily operations.

You will focus on fast, cost-efficient validation, performance improvements, and reliability to ensure consistent behavior across providers and accelerate model delivery.