As a Software Engineer on the ML Infrastructure team, you will design and build the platform for our agent sandboxing platform: the secure, high-performance code execution layer powering our agentic workflows. This system underpins critical applications and research initiatives, and is deployed across both internal and customer-managed environments.

This position requires deep expertise in systems engineering: operating systems, virtualization, networking, containers, and performance optimization. Your work will directly enable agents to execute untrusted or user-submitted code safely, efficiently, and repeatedly, and with fast startup times, strong isolation guarantees, and support for snapshotting and inspection.

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You will:

• Design and build the sandboxing platform for code execution across containerized and virtualized environments.
• Ensure strong isolation, security, and reproducibility of execution across user sessions and workloads.
• Optimize for cold-start latency, memory footprint, and resource utilization at scale.
• Collaborate across security, infra, and product teams to support both internal research use cases and enterprise customer deployments.
• Lead architecture reviews and own projects from design through deployment in fast-paced, cross-functional settings.

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Ideally you'd have:

• 3+ years of experience building high-performance systems software (e.g. OS, container runtime, VMM, networking stack).
• Deep understanding of Linux internals, process isolation, memory management, cgroups, namespaces, etc.
• Experience with containerization and virtualization technologies (e.g., Docker, Firecracker, gVisor, QEMU, Kata Containers).
• Proficiency in a systems programming language such as Go, Rust, or C/C++.
• Familiarity with networking, security hardening, sandboxing techniques, and kernel-level performance tuning.
• Comfort working across infrastructure layers, from kernel modules to orchestration frameworks (e.g., Kubernetes).
• Strong debugging skills and the ability to make performance/security tradeoffs in production systems.

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Nice to haves:

• Familiarity with LLM agents and agent frameworks (e.g., OpenHands, Agent2Agent, MCP).
• Experience running secure workloads in multi-tenant or untrusted environments (e.g., FaaS, CI sandboxes, remote notebooks).
• Exposure to snapshotting and restore techniques (e.g., CRIU, VM snapshots, overlayfs).

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Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:

$156,000—$225,600 USD

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