ZipRecruiter

Log In

1

Deployment Systems Engineer Jobs (NOW HIRING)

G2IT

DevSecOps / Systems Engineer

San Diego, CA · On-site

$140/hr

DevSecOps / Systems Engineer Position Overview G2IT is seeking an experienced DevSecOps / Systems ... This role requires a technically skilled engineer who can support software deployment ...

New

SAIC

HPC Systems Engineer

Charlottesville, VA

Systems Engineer Schedule: Full-Time Shift: Day Job Travel: No Minimum Clearance Required: Top ... The engineer will support the deployment and sustainment of Linux-based High Performance Computing ...

Cornerstone

As a FDE, you will drive successful deployments of AI-powered Workforce Agility products, and ... systems integration, or solutions engineering - with strong proficiency in Python, Java, or ...

SAIC

HPC Systems Engineer

Charlottesville, VA · On-site

Systems Engineer Schedule: Full-Time Shift: Day Job Travel: No Minimum Clearance Required: Top ... The engineer will support the deployment and sustainment of Linux-based High Performance Computing ...

Cornerstone OnDemand

As a FDE, you will drive successful deployments of AI-powered Workforce Agility products, and ... systems integration, or solutions engineering - with strong proficiency in Python, Java, or ...

Nwis

Systems Engineer

Sterling, VA

Requirements management for the design, development, integration, test, and deployment of CUSTOMER ... Certified Systems Engineering Professional (CSEP) * Certified Enterprise Architect (CEA)

next page

Showing results 1-20

People also search for

All JobsDeployment Systems Engineer Jobs

Deployment Systems Engineer information

See salary details

$35.5K

$109.6K

$170K

How much do deployment systems engineer jobs pay per year?

As of Jun 5, 2026, the average yearly pay for deployment systems engineer in the United States is $109,561.00, according to ZipRecruiter salary data. Most workers in this role earn between $80,500.00 and $138,500.00 per year, depending on experience, location, and employer.

What is the difference between Deployment Systems Engineer vs Network Engineer?

AspectDeployment Systems EngineerNetwork Engineer
CertificationsCCNA, CompTIA Network+, Cisco certificationsCCNA, CompTIA Network+, Cisco certifications
Work EnvironmentData centers, cloud environments, enterprise ITNetwork infrastructure, enterprise and data centers
Primary FocusDeploying and managing systems and applicationsDesigning, implementing, and maintaining networks
Industry UsageIT, cloud services, telecommunicationsIT, telecommunications, enterprise networks

While both roles require networking certifications and involve working in IT environments, Deployment Systems Engineers focus on deploying and managing systems and applications, whereas Network Engineers specialize in designing and maintaining network infrastructure. Understanding these differences helps in choosing the right career path or job search focus.

What cities are hiring for Deployment Systems Engineer jobs? Cities with the most Deployment Systems Engineer job openings:
Infographic showing various Deployment Systems Engineer job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 89% Full Time, and 10% Part Time. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $109,561 per year, or $52.7 per hour.
Staff + Senior Software Engineer, Inference Deployment

Staff + Senior Software Engineer, Inference Deployment

Anthropic

San Francisco, CA

Other

Posted 29 days ago

Job description

About the Role

Our mandate is to make inference deployment boring and unattended.

Anthropic serves Claude to millions of users across GPUs, TPUs, and Trainium - and every model update must reach production safely, quickly, and without disrupting service. We're building the systems that make inference deployment continuous and unattended.

As a Software Engineer on the Launch Engineering team, you'll design and build the deployment infrastructure that moves inference code from merge to production. This is a resource-constrained optimization problem at its core: validation and deployment consume the same accelerator chips that serve customer traffic - your deploys compete with live user requests for the same hardware. Every model brings different fleet sizes, startup times, and correctness requirements, so the system must adapt continuously. You'll build systems that navigate these constraints - orchestrating validation, scheduling deployments intelligently, and driving down cycle time from merge to production.

If you've built deployment systems at scale and gravitate toward the hardest problems at the intersection of automation and resource management, this team will give you an outsized scope to work on them.

Responsibilities
  • Own deployment orchestration that continuously moves validated inference builds into production across GPU, TPU, and Trainium fleets, unattended under normal conditions
  • Improve capacity-aware deployment scheduling to maximize deployment throughput against constrained accelerator budgets and variable fleet sizes
  • Extend deployment observability - dashboards and tooling that answer "what code is running in production," "where is my commit," and "what validation passed for this deploy"
  • Drive down cycle time from code merge to production with pipeline architectures that minimize serial dependencies and maximize parallelism
  • Optimize fleet rollout strategies for large-scale deployments across thousands of GPU, TPU, and Trainium chips, minimizing disruption to serving capacity
  • Evolve self-service model onboarding so that new models can be added to the continuous deployment pipeline without Launch Engineering involvement
  • Partner across the Inference organization with teams owning validation, autoscaling, and model routing to integrate deployment automation with their systems
You May Be a Good Fit If You Have
  • 5+ years of experience building deployment, release, or delivery infrastructure at scale
  • Strong software engineering skills with experience designing systems that manage complex state machines and multi-stage pipelines
  • Experience with deployment systems where resource constraints shape the design - whether that's fleet capacity, network bandwidth, hardware availability, or coordinated rollout windows
  • A track record of building automation that measurably improves deployment velocity and reliability
  • Proficiency with Kubernetes-based deployments, rolling update mechanics, and container orchestration
  • Comfort working across the stack - from backend services and databases to CLI tools and web UIs
  • Strong communication skills and the ability to work closely with oncall engineers, model teams, and infrastructure partners
Strong Candidates May Also Have
  • Experience with ML inference or training infrastructure deployment, particularly across multiple accelerator types (GPU, TPU, Trainium)
  • Background in capacity planning or resource-constrained scheduling (e.g., bin-packing, fleet management, job scheduling with hardware affinity)
  • Experience with progressive delivery in systems with long validation cycles: canary/soak testing, blue-green deployments, traffic shifting, automated rollback
  • Experience at companies with large-scale release engineering challenges (mobile release trains, monorepo deployments, multi-datacenter rollouts)
  • Experience with Python and/or Rust in production systems

Apply