Platform Engineer

Description

The company is building an AI engineering agent for physical-world products. It is designed to join existing engineering teams in the role of a human engineer.

Today the agent works at about the level of a junior mechanical and electrical engineer. It reasons quantitatively about product design and uses the complex engineering software that human colleagues rely on.

The agent is built on a custom agent harness, structured design representation, continual skill learning, and small custom post-trained models (SFT and RLVR) trained on proprietary semi-synthetic datasets and environments.

The long-term goal is engineering ASI. The company recently closed a $50 million Series A led by NEA. The board now includes former General Electric CEO Jeff Immelt, and the investor group added AI researchers from Anthropic and Nominal alongside existing angel investors from Google and OpenAI.

Interview process: a 30-minute introductory call, a 45-minute behavioural interview, a 60-minute technical interview, and a 30-minute interview with the CEO.

You will own the infrastructure that lets the agent run reliably inside customer environments, and you will drive changes to the BYOC deployment architecture. The work covers distributed systems, cloud infrastructure and security, with substantial say over how the agent is deployed and scaled.

Responsibilities

• Build and maintain the BYOC deployment model for the agent in customer environments, covering how components are distributed, installed and updated, and how they connect to the company's central systems.
• Design and run Kubernetes infrastructure that works reliably across several clouds and varied customer setups.
• Build deployment tooling with Helm, GitOps or similar approaches so installation and operations are repeatable.
• Create secure, reliable update and upgrade paths that work in restricted or locked-down networks.
• Partner with product, research and forward-deployed engineers to turn their needs into platform capabilities.
• Find and fix bottlenecks in performance, reliability and operability in live deployments.
• Set up observability, monitoring and debugging practices for distributed systems the company does not fully control.

Requirements

• You have built and run Kubernetes systems in production.
• You have designed infrastructure with IaC tools such as Terraform, Pulumi or similar.
• You have set up CI/CD or GitOps workflows that deploy systems across environments.
• You work comfortably across AWS, GCP and Azure and understand their core building blocks.
• You have worked on systems where security and networking are central, such as VPCs, identity and encryption.
• You take problems from start to finish and are comfortable in ambiguous, 0-to-1 environments.
• You keep evolving how you use AI in your own technical work.

Conditions

Location: remote in the US or Canada, or in the San Mateo, CA office. Remote staff spend one week in every six working on-site in San Mateo.

Relocation support is available.

Benefits

• Competitive salary and meaningful equity ownership.
• Healthcare, dental and vision coverage.
• 401(k) match.
• Unlimited PTO.