Raft

Full-stack Engineer

• Drive architectural decisions across the [R]AIMS platform, evaluating tradeoffs across performance, scalability, security, and maintainability and building alignment across engineering and product stakeholders
• Lead major technical epics from conception through delivery, decomposing ambiguous problems into executable plans and keeping cross-functional teams moving with clarity and momentum
• Simplify and rationalize distributed system architecture as the platform scales, reducing incidental complexity and improving operational reliability without sacrificing capability
• Optimize platform performance across both edge and cloud deployment targets, identifying and resolving bottlenecks in data-intensive, latency-sensitive operational environments
• Establish strong engineering foundations and reusable technical patterns that improve developer productivity and code quality across the team
• Mentor engineers at multiple levels, conducting design reviews, providing substantive code feedback, and actively elevating technical execution across the platform
• Partner with AI/ML engineers on model integration, inference optimization, and the operational deployment of agentic workflows within [R]AIMS
• Engage directly with customers and program stakeholders at operationally demanding environments across the Department of Defense, representing Raft’s technical capabilities with credibility and clarity

Lead AI/ML Software Engineer

• You will be part of the team building a data focused product that is built on best of breed open source solutions like Kafka, Trino, DuckDB, and Superset.
• You will work both on backend and frontend to build end-to-end features that make interacting with data easier enabling users to be able to tell a data story through our product.

Principal Software Engineer

Machine Learning Engineer

• Design, build, and maintain secure, scalable MLOps infrastructure and deployment pipelines for production ML systems
• Help mature Raft’s internal ML platform and model lifecycle capabilities, including model packaging, registry/catalog workflows, deployment, monitoring, and operational support
• Deploy and manage machine learning workloads on Kubernetes, including GPU-enabled clusters
• Support model serving and inference infrastructure for a range of ML use cases, including traditional ML, computer vision, speech/audio, and LLM-based systems
• Build and maintain CI/CD workflows for ML services, model artifacts, and platform components
• Partner closely with ML engineers, software engineers, and product teams to move models from experimentation to reliable operational deployment
• Improve observability, reliability, security, and maintainability across ML infrastructure and services
• Help evaluate and standardize runtime patterns, serving frameworks, and deployment architectures for production ML workloads
• Contribute to infrastructure decisions across edge, on-prem, and cloud-hosted deployment environments
• Support compliance-driven deployment practices and secure software supply chain requirements in defense environments
• Get hands-on with customers at the most forward-leaning places in the Department of War

Lead AI/ML Software Engineer

Principal Software Engineer

Principal MLOps Engineer