Software Engineer, Infrastructure & Platform
New
J
JobgetherAI Infrastructure
Candidates must be based in the United StatesFull-TimeMiddle
Salary$110,000–$160,000 annually, depending on experience and location.
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Job Details
- Experience
- 3–5+ years
- Required Skills
- AWSDockerPythonGCPKubernetesLinuxTerraformDistributed Systems
Requirements
- 3–5+ years of professional software engineering experience, particularly in backend, infrastructure, platform, SRE, or distributed systems engineering.
- Strong programming skills in Python and experience developing production-quality software.
- Proven experience designing and operating backend services, APIs, or distributed systems.
- Hands-on experience with Docker, Kubernetes, virtual machines, or comparable container and orchestration technologies.
- Experience working with AWS, GCP, or similar cloud infrastructure platforms.
- Strong understanding of Linux systems, networking, authentication, permissions, and infrastructure security.
- Experience with Infrastructure as Code and automation tools such as Terraform.
- Excellent debugging and troubleshooting abilities across application, infrastructure, and networking layers.
- Ability to build systems that are reproducible, observable, scalable, reliable, and secure.
- Comfort working through ambiguous technical challenges where requirements and architecture may change rapidly.
Responsibilities
- Design and build sandboxed evaluation environments that allow AI models to safely execute code, interact with tools and services, and perform complex tasks.
- Develop backend services and infrastructure that support large-scale, repeatable AI and agentic evaluations.
- Build agent scaffolding and evaluation harnesses covering tool-use loops, context management, retries, state management, token budgets, and multi-agent or subagent workflows.
- Provision and orchestrate isolated environments using technologies such as Docker, Kubernetes, virtual machines, and cloud infrastructure.
- Design secure approaches to networking, permissions, credentials, secrets management, and resource isolation for model-driven environments.
- Develop APIs, internal tools, and automation that enable researchers, engineers, and subject-matter experts to efficiently create and execute evaluations.
- Improve evaluation reliability and reproducibility through logging, observability, snapshotting, debugging capabilities, and automated testing.
- Build infrastructure capable of running thousands of evaluation tasks reliably while capturing the artifacts and telemetry required to analyze model behavior.
- Partner with analysts, red teamers, and technical experts to translate sophisticated evaluation concepts into dependable engineering systems.
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