Distributed Systems ML Infrastructure Engineer Washington, DC Metro; Denver, CO Metro; or Colorado Springs

Washington, DC Metro;
Denver, CO Metro; or Colorado Springs, CO - Hybrid/Remote

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.

Open Teams exists to make ownership possible.

Founded by Travis Oliphant, creator of Num Py and Sci Py, and built by people with deep roots across the open-source ecosystem, including Num Py, Sci Py, 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.

Location: Washington, DC;
Denver, CO; or Colorado Springs, CO preferred (hybrid). Highly qualified candidates outside these locations may also be considered for unclassified work.

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

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.

This position is contingent upon contract award. Travel of up to 15% may be required, primarily to Government facilities and between company locations. Unclassified work may be performed remotely, while classified promotion and validation activities require onsite work in an accredited facility and the appropriate security clearance.

• Design and implement platform services for workflow orchestration, data ingest, and results management, exposed through documented APIs with no proprietary front end
• Implement and operate model gateway and serving services that route invocations to approved managed model services with policy enforcement, usage accounting, and audit logging
• Maintain a documented provider abstraction so the platform runs on AWS-native managed services where appropriate while remaining deployable across other cloud and dedicated environments
• Deploy platform releases into classified host environments, perform data source integration, and execute validation procedures on a recurring promotion cadence
• Verify environment parity after each promotion
• Size and validate the platform against documented workload models, and verify capacity and performance by load test
• Constrain platform dependencies to services confirmed available in the target environments, and gate any development-only dependency behind feature flags
• Reproduce high-side defects on the low side through sanitized feedback paths and fix them where the full toolchain is available

• U.S. citizenship and eligibility to obtain and maintain a U.S. security clearance
• 6+ years of experience in distributed systems, platform engineering, infrastructure engineering, or a related software engineering role
• Production experience operating Kubernetes and containerized workloads on a major cloud platform
• Experience with managed Kubernetes services such as Amazon EKS or an equivalent platform
• Experience supporting machine learning workloads in…