Staff Engineer

Raft Company Website•Colorado Springs, CO

8d•$150,000 - $200,000•Hybrid

About The Position

The Staff Engineer for [R]AIMS is a senior technical leader responsible for evolving the architecture, execution, and engineering rigor of Raft’s AI Mission System. The role is hands-on in the codebase while setting technical direction and raising engineering quality across the team. The Staff Engineer partners with platform leadership, product, and delivery teams to guide architectural decisions, lead technical initiatives from conception through delivery, and establish reusable engineering patterns. The work spans distributed systems, AI/ML platform engineering, Kubernetes-native infrastructure, and data-intensive application development. It involves balancing rapid mission delivery with long-term platform integrity, building production systems with complex integrations, troubleshooting distributed systems, leading design reviews, and making pragmatic decisions amid ambiguity. The position is hybrid in Colorado Springs, Colorado, with up to 25% travel. U.S. citizenship is required, all work must be performed within the continental United States, and a Secret clearance is required to start. The employee must be eligible and willing to obtain a Top Secret/SCI clearance and able to obtain Security+ certification within the first 90 days of employment.

Requirements

At least 6 years of hands-on experience building and shipping production software systems across frontend, backend, infrastructure, and machine learning.
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.
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 with real performance constraints.
Demonstrated technical leadership of large cross-functional engineering initiatives, with clear ownership and accountability for outcomes.
Strong system design and architecture decision-making ability, including making decisions under 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; ability to obtain and maintain a Top Secret/SCI clearance.
Secret clearance required to start; must be eligible for and willing to obtain a Top Secret/SCI clearance.
All work must be conducted within the continental United States.

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 building 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.

Responsibilities

Drive architectural decisions across the [R]AIMS platform, weighing performance, scalability, security, and maintainability and building alignment with engineering and product stakeholders.
Lead major technical epics from conception through delivery by turning ambiguous problems into executable plans and coordinating cross-functional teams.
Simplify and rationalize distributed system architecture as the platform scales, reducing 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, conduct design reviews, provide substantive code feedback, and elevate technical execution across the platform.
Partner with AI/ML engineers on model integration, inference optimization, and operational deployment of agentic workflows within [R]AIMS.
Engage with customers and program stakeholders in Department of Defense operational environments and represent Raft’s technical capabilities.
Help ensure the platform has a coherent, well-documented architecture and that major initiatives deliver with high engineering quality and clear technical rationale.