Distributed Systems ML Infrastructure Engineer

OpenTeams•Washington, DC

20h•$145,000 - $250,000•Hybrid

About The Position

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.

Requirements

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 production, such as model serving, GPU scheduling, or large-scale data and evaluation pipelines
Experience designing, building, or operating distributed services that support reliability, scalability, and performance requirements
Proficiency in Python, Go, or a comparable programming language
Experience with infrastructure-as-code and deployment tools such as Terraform, Helm, or equivalent technologies
Experience designing API-first services and implementing documented interface specifications
Experience testing platform capacity and performance against expected workload requirements
Ability to document technical interfaces, deployment procedures, architectural decisions, and validation results
Bachelor’s degree in computer science, engineering, or a related field, or equivalent practical experience

Nice To Haves

Active TS/SCI clearance with CI polygraph
Hands-on experience deploying or operating software in classified, air-gapped, or limited-connectivity environments
Experience with classified cloud environments, including AWS Secret or Top Secret regions
Experience building or operating model gateways, LLM routing layers, inference services, or inference-brokering platforms
Familiarity with software promotion into classified environments, cross-domain transfer processes, and release packaging
Experience with agentic workflow frameworks or the orchestration of multi-step AI pipelines
Experience supporting GPU-accelerated workloads and distributed model inference
Experience designing vendor-agnostic platforms that operate across multiple cloud or dedicated environments
Experience supporting rapid prototyping programs or defense innovation initiatives

Responsibilities

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