Senior Software Engineer - Cloud Infrastructure

About the role

Everything Applied ships runs on infrastructure we build. From the daily, large-scale simulations that test autonomous systems to the enterprise AI workloads behind Dana, our cloud infrastructure team builds and maintains the platform that product engineering uses to ship, operate, and scale their applications. We are cloud-native and cloud-agnostic across all cloud providers. We run on Kubernetes, manage everything as infrastructure as code, and own the full stack, including compute, networking, file system, blob storage, resource scaling, observability, security, and CI/CD. Most platform teams own a slice of that. We own the whole thing.

We also build the core infrastructure platform that powers Dana, Applied's AI platform for enterprises in physical industries, bringing apps, agents, and data together on one governed platform. You'll help build Dana's core infrastructure and everything customers touch to build, deploy, share, and govern apps. Dana is multi-tenant and serves critical enterprise workloads, so scale and reliability tradeoffs are part of the job. The compute and data generation scale of our workloads, from large-scale simulation to agentic execution, pushes the boundaries of standard cluster deployments, and you'll be at the forefront of building out this system and ensuring its reliability.

Owning the whole stack makes this as much a platform-building and solutions-architecture role as a hands-on infrastructure role. You'll work embedded with engineers across the entire org and support a variety of customer deployments, and you'll be trusted to make architectural calls, own incidents end-to-end, and drive infrastructure decisions rather than just execute tickets.

In this role, you will

• Design, build, and operate our multi-cluster Kubernetes infrastructure (compute, networking, storage, autoscaling, observability, and security) with high reliability across all cloud providers

• Build multi-tenant platform primitives: tenant-safe storage, shared secrets, RBAC, and workload identity

• Design and orchestrate secure, sandboxed execution environments for agentic and untrusted workloads

• Act as a solutions architect for internal platform customers, turning their scaling and reliability needs into concrete infrastructure designs and driving them to production

• Improve developer effectiveness by spotting the friction nobody else has bothered to fix and building the tooling that removes it

• Debug and profile the awkward edge cases in distributed systems, and own production end-to-end: incident command, postmortems, and the follow-through that prevents recurrence

• Mentor engineers and raise the technical bar through design reviews and thoughtful collaboration

You may be a good fit if you

• Have 5+ years of experience building and operating large-scale infrastructure, platform, SRE, or DevOps systems

• Have experience with container orchestration frameworks such as Kubernetes

• Have deep experience with at least one major cloud provider (AWS, GCP, Azure or OCI)

• Write production-quality code and are an expert in at least one of Go, Python, Rust, or C++, and comfortable picking up whatever the problem needs

• Are fluent with Infrastructure as Code and GitOps workflows (Terraform, OpenTofu, Pulumi, Crossplane, or similar)

• Communicate and collaborate well across teams: aligning on interfaces, navigating tradeoffs, and driving cross-team execution

• Hold a BS in Computer Science or a related field

Strong candidates may also have

• Experience with serverless or scale-to-zero container platforms such as Knative, Cloud Run, or KEDA-based systems for running multi-tenant application workloads

• Depth in sandboxing and workload isolation: Linux namespaces, cgroups, seccomp, gVisor, Firecracker/Kata, or comparable multi-tenant isolation designs

• Depth in cluster and cloud networking: CNI (e.g., Cilium), eBPF, NetworkPolicy, service mesh, cross-cloud private connectivity

• Deep multi-cluster or multi-region Kubernetes experience running diverse workloads at scale, from large batch and data-processing jobs to GPU and agentic workloads, including scheduling and autoscaling systems such as Karpenter, Kueue, or Volcano

• Platform security experience: admission control, least-privilege IAM, workload identity (OIDC/SPIFFE), image provenance and supply-chain hardening

• Incident command experience for customer-facing production systems

• Experience building enterprise AI infrastructure or delivering platform solutions to internal or enterprise customers

• Contributions to open source infrastructure tooling