Staff AI/ML Platform Engineer

Raft; Reston (VA); Remote; USD 180,000 - 230,000; Full time

Benefits offered by this job

Healthcare

Dental and Vision coverage

401(k) and company match

Paid time off + holidays

Education & training benefits

Referral bonuses

Job summary

Raft, a U.S.-based defense tech company, seeks a Staff Engineer for [R]AIMS to lead architecture, ensure scalable production systems, and guide engineering rigor across the platform. You will write production-quality code, shape technical direction, and partner with platform leadership, product, and delivery teams to drive major epics from conception through delivery in a Kubernetes-native environment.

Candidates should have strong distributed systems experience, cloud-native expertise, and the

Qualifications

• Hands-on experience building production software systems at scale.
• Ability to design and evolve distributed architectures with clear ownership.
• Strong communication across engineering, product, and leadership.

Responsibilities

• Drive architectural decisions across the platform balancing performance and maintainability.
• Lead major epics from conception to delivery with cross-functional teams.
• Simplify distributed systems as the platform grows while preserving capability.
• Collaborate with AI/ML engineers on model integration and inference deployment.
• Mentor engineers and conduct design reviews to elevate technical execution.
• Engage with DoD stakeholders to represent Raft's capabilities with credibility.

Skills

Distributed systems

Kubernetes

Cloud-native

System design

Technical leadership

Communication skills

Security+ readiness

Tools

Kafka

Kafka Streams

Apache Flink

Job description

Raft, a U.S.-based defense tech company, seeks a Staff Engineer for [R]AIMS to lead architecture, ensure scalable production systems, and guide engineering rigor across the platform. You will write production-quality code, shape technical direction, and partner with platform leadership, product, and delivery teams to drive major epics from conception through delivery in a Kubernetes-native environment.

Candidates should have strong distributed systems experience, cloud-native expertise, and the