Deploying and maintaining on-prem and VPC instances of the systems, often with complex enterprise constraints (e.g. air-gapped, custom auth, strict latency targets).

Building tooling to make deployments faster, more reliable, and easier to debug.

Creating diagnostics, dashboards, and eval harnesses to measure performance and track regressions across environments.

Writing crisp internal documentation and playbooks for repeatable deployments.

Collaborating closely with customers, support, and core engineers to identify high-impact improvements.

Acting as a feedback loop to the product and infra teams—surfacing usability gaps, unmet needs, and opportunities to deepen customer value.

Working directly with the founders and customers to shape the product direction and engineering strategy

You have 3 to 6 years of experience deploying software in production settings—ideally with enterprise or regulated customers.

You're excellent with Python and can navigate Bash and infrastructure as needed.

You're very comfortable with Kubernetes.

Tools: Build your own tools as needed—like a quick Streamlit app to test hypotheses or create a dataset.

Approach: A quantitative approach to building products. Ability to debug, experiment, and iterate fast. You should be comfortable getting hands-on with the full development lifecycle, from ideation to shipping to users.

Prior experience founding a company or building products at early stages

You are ambitious and driven, and care a lot about doing great work with great people

You keep up with the latest developments in ML/AI