Software Engineer, Infrastructure & Platform
New
1
10a LabsAI Safety and Intelligence
Fully remote, U.S.-basedFull-TimeMiddle
Salary$110K–$160K, depending on experience and location
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Job Details
- Experience
- 3–5+ years of professional software engineering experience
- 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 building production-quality software.
- Experience designing and operating backend services, APIs, or distributed systems.
- Hands-on experience with Docker, Kubernetes, virtual machines, or other container/orchestration technologies.
- Experience working with AWS, GCP, or similar cloud infrastructure.
- Strong understanding of Linux systems, networking, authentication, permissions, and infrastructure security.
- Experience with infrastructure-as-code or automation tools such as Terraform.
- Strong debugging skills and comfort diagnosing failures across application, infrastructure, and networking layers, especially in agentic loops.
- Ability to build systems that are reproducible, observable, scalable, and secure.
- Comfort working on ambiguous technical problems where the architecture and requirements may evolve quickly.
- Interest in AI systems, agentic workflows, AI security, or model evaluations.
Responsibilities
- Design and build sandboxed evaluation environments where AI models can safely execute code, use tools, interact with services, and complete complex tasks.
- Build backend services and infrastructure supporting large-scale, repeatable AI and agentic evaluations.
- Develop agent scaffolding and evaluation harnesses, including tool-use loops, context management, retries, state management, token budgets, and multi-agent or subagent workflows.
- Build systems for provisioning and orchestrating isolated environments using technologies such as Docker, Kubernetes, VMs, and cloud infrastructure.
- Design secure approaches to networking, permissions, secrets, credentials, and resource isolation for model-driven environments.
- Develop APIs, internal tools, and automation that allow researchers, engineers, and subject-matter experts to create and run evaluations efficiently.
- Improve the reliability and reproducibility of evaluations through logging, observability, snapshotting, debugging tools, and automated testing.
- Build systems capable of running thousands of evaluation tasks reliably and capturing the artifacts and telemetry needed to understand model behavior.
- Partner with analysts, red teamers, and domain experts to translate complex evaluation ideas into robust technical systems.
- Investigate failures across the evaluation stack and distinguish between model limitations and infrastructure, harness, or environment failures.
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