Lead AI/ML Software Engineer

New

RaftDefense technology

Remote within the U.S.; All work must be conducted within the continental U.S.Full-TimeLead

Salary180,000 - 230,000 USD per year

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Job Details

Experience: 6+ years of hands-on experience building and shipping production software systems across the full stack (frontend, backend, infrastructure, and ML)
Required Skills: KafkaKubernetesMachine LearningDistributed Systems

Have 6+ years of hands-on experience building and shipping production software across frontend, backend, infrastructure, and ML.
Demonstrate strong software engineering fundamentals and the ability to design and evolve complex systems that perform reliably at scale.
Have experience designing and evolving distributed systems, including service decomposition, inter-service communication, fault tolerance, and observability.
Have hands-on experience with Kubernetes and cloud-native platform architecture in production.
Have experience building data-intensive or AI-enabled production systems with operational users and performance constraints.
Demonstrate technical leadership of large, cross-functional engineering initiatives with clear ownership and accountability.
Have strong system design and architecture decision-making ability, including making decisions with incomplete information.
Have some experience or exposure to training, fine-tuning, or deploying machine learning models in production.
Be able to obtain Security+ certification within the first 90 days of employment.
Preferred experience includes AI/ML infrastructure or agentic systems, Kafka or Apache Flink, platform engineering, real-time inference, and secure regulated or mission-critical systems.

Drive architectural decisions across the AIMS platform, evaluating performance, scalability, security, and maintainability tradeoffs.
Lead major technical epics from conception through delivery, turning ambiguous problems into executable plans.
Simplify distributed-system architecture and improve operational reliability as the platform scales.
Optimize platform performance across edge and cloud deployment targets.
Establish reusable technical patterns that improve developer productivity and code quality.
Mentor engineers through design reviews and substantive code feedback.
Partner with AI/ML engineers on model integration, inference optimization, and operational deployment of agentic workflows.
Engage with customers and program stakeholders in Department of Defense operational environments.

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