Lead Engineer

Raft•Boston, CO

2d•$180,000 - $230,000•Remote

About The Position

Raft is seeking a hands-on technical leader to shape the architecture, execution, and engineering rigor of its [R]AIMS AI Mission System. The Lead AI/ML Software Engineer will write production code while setting technical direction, leading major engineering efforts, and establishing reusable patterns that support the platform’s growth. The role works closely with platform leadership, product, delivery teams, AI/ML engineers, customers, and program stakeholders. The position spans distributed systems, AI/ML platform engineering, Kubernetes-native infrastructure, and data-intensive application development. The engineer will make architecture and tradeoff decisions, guide complex initiatives from conception through delivery, improve performance across edge and cloud deployments, and help strengthen engineering quality and developer productivity. The work also includes customer engagement in demanding Department of Defense environments. This is a remote U.S.-based role, and all work must be conducted within the continental United States. Candidates must be U.S. citizens and eligible and willing to obtain and maintain a Top Secret/SCI clearance; no clearance is required to start. Security+ certification must be obtained within the first 90 days. Up to 25% travel is expected. Salary range is $180,000–$230,000.

Requirements

At least 6 years of hands-on experience building and shipping production software systems across the full stack, including 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 with 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 ability, 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 eligible for and willing to obtain and maintain 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 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 the [R]AIMS platform, evaluating performance, scalability, security, and maintainability tradeoffs and aligning engineering and product stakeholders.
Lead major technical epics from conception through delivery, turning ambiguous problems into executable plans and maintaining cross-functional momentum.
Simplify and rationalize distributed system architecture as the platform scales, reducing complexity and improving operational reliability.
Optimize platform performance for edge and cloud deployments, including bottlenecks in data-intensive and latency-sensitive environments.
Establish reusable engineering patterns and foundations 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.
Engage directly with customers and program stakeholders in Department of Defense environments, clearly representing Raft’s technical capabilities.