Staff Engineer

Raft•Colorado Springs, CO

7d•Hybrid

About The Position

Raft is seeking a Staff Engineer for its [R]AIMS (Raft AI Mission System) platform, a system that enables operators and engineers to build, deploy, and govern AI-powered mission workflows. This U.S.-based role is for a hands-on senior technical leader who will evolve the platform’s architecture, execution, and engineering rigor. The engineer will contribute directly to the codebase while setting technical direction, leading major engineering efforts, and establishing reusable patterns and high-quality practices across the team. The role spans distributed systems, AI/ML platform engineering, Kubernetes-native infrastructure, and data-intensive application development. The Staff Engineer will work with platform leadership, product, delivery teams, AI/ML engineers, customers, and program stakeholders. The position is hybrid in Colorado Springs, Colorado, with up to 25% travel. Employment requires U.S. citizenship, work conducted within the continental U.S., and a Secret clearance at start; the employee must be eligible and willing to obtain and maintain a Top Secret/SCI clearance. The employee must also be able to obtain Security+ certification within the first 90 days.

Requirements

At least 6 years of hands-on experience building and shipping production software systems across frontend, backend, infrastructure, and ML.
Strong software engineering fundamentals and demonstrated ability to design, build, and evolve complex systems that perform reliably at scale.
Exceptional technical communication skills and ability to lead through influence across engineering, product, and leadership stakeholders.
Proven experience designing and evolving distributed systems, including service decomposition, inter-service communication, fault tolerance, and observability.
Strong hands-on experience with Kubernetes and cloud-native platform architecture in production environments.
Experience building data-intensive or AI-enabled production systems for operational users and real performance constraints.
Demonstrated technical leadership of large, cross-functional engineering initiatives, with ownership and accountability for outcomes.
Strong system design and architecture decision-making skills, including making sound decisions with incomplete information.
Some experience or exposure to training, fine-tuning, or deploying machine-learning models in production contexts.
Ability to obtain Security+ certification within the first 90 days of employment.
U.S. citizenship required; must be able to obtain and maintain a Top Secret/SCI clearance.
Secret clearance required to start; must be eligible and willing to obtain a Top Secret/SCI clearance.

Nice To Haves

Experience building AI/ML infrastructure or agentic systems, including orchestration frameworks, tool-use patterns, and production LLM integration.
Experience with streaming and event-driven architectures, particularly Kafka, Kafka Streams, or Apache Flink.
Experience with platform engineering and internal developer tooling, including golden-path frameworks, shared libraries, and developer experience improvements.
Experience with real-time inference or operational AI systems in latency-sensitive environments.
Experience building secure, compliant systems for regulated or mission-critical environments, including familiarity with IL4/IL5/IL6 requirements or RMF processes.
Prior work in defense, national security, or classified program environments.
Active clearance preferred.

Responsibilities

Drive architectural decisions across [R]AIMS, weighing performance, scalability, security, and maintainability and building alignment with engineering and product stakeholders.
Lead major technical epics from conception through delivery, turning ambiguous problems into executable plans and keeping cross-functional teams aligned and moving.
Simplify and rationalize distributed-system architecture as the platform scales, reducing incidental complexity and improving operational reliability.
Optimize platform performance across edge and cloud deployments, identifying and resolving bottlenecks in data-intensive, latency-sensitive environments.
Establish engineering foundations and reusable technical patterns that improve developer productivity and code quality.
Mentor engineers through design reviews, substantive code feedback, and efforts to elevate technical execution across the platform.
Partner with AI/ML engineers on model integration, inference optimization, and operational deployment of agentic workflows.
Engage with customers and program stakeholders in demanding Department of Defense environments, communicating Raft’s technical capabilities credibly and clearly.