USA
Distributed Systems ML Infrastructure Engineer
About this role
Who We Are Every organization runs on intelligence: years of accumulated knowledge, decisions, and context. As AI takes on more of that work, companies face a choice: rent that intelligence from vendors who keep the data, the context, and the results, or own it. OpenTeams exists to make ownership possible. Founded by Travis Oliphant, creator of NumPy and SciPy, and built by people with deep roots across the open-source ecosystem, including NumPy, SciPy, PyTorch, and Jupyter, we help enterprises and governments build AI they control, govern, and evolve themselves.
If that sounds like your kind of work, we'd like to meet you. Distributed Systems ML Infrastructure Engineer Location: Washington, DC; Denver, CO; or Colorado Springs, CO preferred (hybrid). Highly qualified candidates outside these locations may also be considered for unclassified work. Work Authorization: U.S. citizenship required Clearance: An active TS/SCI clearance with CI polygraph is strongly preferred. Candidates without an active clearance may be considered for unclassified work but must be eligible to obtain and maintain a U.S.
security clearance. Salary Range: $145,000–$250,000 USD, dependent on experience level and location About the Role We're looking for a Distributed Systems and ML Infrastructure Engineer to build the core services of a containerized, API-first AI platform. This is a role for someone who wants to build the thing itself, not integrate someone else's. You design and implement the services the platform runs on — workflow orchestration, data ingestion, results management, model serving, policy enforcement, usage accounting, audit logging.
Those services have to hold up across cloud, dedicated, isolated, and limited-connectivity deployments, which means portability and operability are design constraints from the first commit rather than problems handed to someone downstream. Development happens primarily on unrestricted infrastructure with an open-source toolchain. Engineers with the right access also carry releases into controlled production environments, integrate data sources there, and validate the platform in place — so there's a path to seeing your work through to where it actually runs.