Senior Software Engineer (AI Inference & Runtime Platform)

Accountabilities:

You will own the core infrastructure that enables AI models and agent-generated workloads to run securely, efficiently, and reliably in production. Your responsibilities will include:

• Manage the model-serving infrastructure for open-weight large language models, including deployment, configuration, cold-start optimization, model-specific service-level objectives (SLOs), upgrades, and canary releases.

• Operate and improve the Kubernetes infrastructure supporting inference and sandbox workloads, including custom operators, custom resource definitions (CRDs), autoscaling, GPU scheduling and sharing, and node lifecycle management.

• Own the stateful data plane end to end, ensuring reliable operation, documented recovery procedures, and regularly tested restoration processes.

• Develop and maintain the secure agent-sandboxing runtime and host-side control plane, covering lifecycle management, command execution, snapshots, forking, teardown, usage metering, and isolation-boundary security.

• Build and maintain FastAPI control-plane services, infrastructure-as-code configurations using Terraform/OpenTofu and Bicep, and dashboards that support effective operations.

• Provide the infrastructure foundation used by backend engineering teams, collaborating across services and investigating issues that span the platform and application layers.

• Establish and maintain observability, performance, reliability, and operational practices that help teams diagnose problems and make informed decisions.

• Contribute to an open-source-oriented development process through reviewed pull requests, comprehensive documentation, reproducible builds, and releases as components mature.

Requirements:

You are an experienced systems engineer with a strong production background, sound security judgment, and the ability to operate complex infrastructure under real-world conditions. You bring most of the following qualifications:

• At least 5 years of experience delivering production systems using a systems programming language. Strong Rust expertise is valuable, while deep experience in Go, C/C++, or Zig combined with a genuine willingness to work in Rust is also relevant.

• Solid understanding of asynchronous runtimes, memory safety, and low-level debugging, including troubleshooting at the system-call boundary.

• Hands-on experience operating Kubernetes workloads that other teams depend on, including controllers or operators, scheduling, autoscaling, and node lifecycle management.

• Proven ability to threat-model isolation boundaries involving technologies such as namespaces, cgroups, seccomp, and hypervisors, including assessing what an untrusted guest could observe, manipulate, or exhaust.

• Practical knowledge of secure AI-agent execution, including least-privilege access, preventing credentials from entering sandbox environments, maintaining audit trails, and requiring human approval for actions that modify systems of record.

• Experience deploying or operating open-weight LLM inference infrastructure, such as vLLM, SGLang, or comparable serving systems, and packaging models into reliable, metered production endpoints.

• Strong distributed-systems performance skills, including measurement-driven optimization, latency analysis, and the ability to demonstrate improvements with concrete metrics.

• Practical experience with technologies such as Rust and Tokio, Python and FastAPI, Kubernetes operators, controller-runtime, Kubebuilder, CRDs, KEDA, Karpenter, GPU device plugins, or Dynamic Resource Allocation (DRA), along with a willingness to learn unfamiliar components.

• Familiarity with isolation technologies such as Firecracker, Kata Containers, gVisor, or equivalent solutions.

• Experience with secrets management and egress controls, including Vault- or KMS-class systems, as well as stateful technologies such as pgvector, Qdrant, or Neo4j, with strong backup and failover practices.

• Comfort operating across AWS, Azure, GCP, and managed GPU cloud environments, using infrastructure-as-code tools such as Terraform/OpenTofu and Bicep, along with observability tooling such as OpenTelemetry.

• A bachelor's degree is required; a master's degree is a plus.

• Ability to travel twice a year for company summits.

• Current authorization to work full-time in the United States. Employment visa sponsorship or transfer is not available.

• Willingness to complete a written take-home assignment followed by a live, two-hour technical session with the engineering team.

• Applicants may apply to a maximum of two roles at a time. Applying to more than two roles within a six-month period results in automatic disqualification.

Benefits:

• Salary: $140,000–$230,000, with compensation determined by experience, capabilities, and location.

• Equity: Equity participation in an early-stage company.

• Bonus: Eligibility for performance-related bonus compensation.

• Healthcare: Health insurance with meaningful coverage for dependents.

• Flexible time off: Paid time off designed to support flexibility and work-life balance.

• Remote work: Fully remote working culture, with a cluster of teammates based in Seattle.

• Mission-driven work: The opportunity to contribute to AI transformation across critical industries through impactful applications in energy, sustainability, climate, logistics, and related fields.

• Engineering ownership: The opportunity to shape foundational AI infrastructure, work across the stack, and contribute to open-source projects.

• Company summits: Opportunities to connect with teammates in person twice per year.