Member of Technical Staff - Infrastructure

As our Founding Infrastructure Engineer, you'll define and own the production inference platform behind this new layer of intelligence.

You'll work alongside a founding team who published in Nature, who's research was funded by Google Deepmind and with experience building and deploying AI systems in Fortune 500s. This is a deeply hands-on role: you'll build core systems from the ground up, make foundational architectural decisions, and help shape the engineering culture of the company.

The platform must operate wherever the world's most demanding enterprises need it: from our managed cloud to customer-controlled infrastructure and fully air-gapped environments. You'll design the architecture that makes this possible without fragmentation, while preserving performance, reliability, and security across all deployments.

What You'll Do

• Own our production inference platform across model serving, orchestration, deployment, observability, and operations.
• Optimize how workloads are batched, cached, scheduled, and routed while balancing latency, throughput, quality, and cost.
• Build reliable infrastructure for continuously processing high-volume data, including backfills and safe reprocessing.
• Design one portable platform that runs consistently across managed cloud, customer-owned infrastructure, and fully air-gapped environments.
• Make multi-tenancy a foundational property of the system, with strong per-customer isolation across data, compute, identity, and operations.
• Own the model lifecycle, including versioning, evaluation, rollout, monitoring, and rollback.
• Define how data is retained, recovered, governed, and securely deleted across every deployment model.
• Work directly with the founders to shape our technical strategy and infrastructure roadmap.

What We're Looking For

• Experience operating machine-learning or similarly compute-intensive distributed systems in production.
• Strong experience with cloud infrastructure, containers, orchestration, and observability.
• Experience designing multi-tenant platforms with rigorous isolation and security boundaries.
• Experience building reliable data-processing systems or continuous data pipelines.
• A record of owning production systems from architecture through operation.
• Familiarity with Infrastructure as Code (IaC) and tools such as Terraform
• Comfort working hands-on with substantial autonomy and without an established blueprint.

Useful

• Experience operating and optimizing GPU workloads.
• Experience with Kubernetes, Triton, vLLM, TensorRT, Ray, or comparable technologies.
• Experience deploying into customer-controlled, private-cloud, or fully air-gapped environments.
• Experience with enterprise security, data governance, or production ML evaluation.
• Experience as a founding or early infrastructure engineer.