AI/ML Engineer

Raft Company Website•Rome, NY

31d•$170,000 - $220,000•Hybrid

About The Position

Raft is a defense tech company focused on AI/ML and data solutions for U.S. military and government agencies. They are a leader in autonomous data fusion and Agentic AI, specializing in Distributed Data Systems, Platforms at Scale, and Complex Application Development. Raft's headquarters are in McLean, VA, and they serve federal and public agencies with innovative technology. Their AI platform, [R]AIMS, helps users build, deploy, and govern AI-powered mission workflows. Raft is expanding its AI/ML team in Rome, NY, to support customers and is seeking an AI/ML Engineer to contribute to model development, evaluation, and operational AI delivery. This role involves working directly on model development, leveraging the [R]AIMS platform to accelerate experimentation, evaluation, deployment, and operational transition. It's a hands-on position for an engineer interested in building real-world AI systems with direct mission impact. The engineer will collaborate with platform engineers, AI leadership, and mission stakeholders to move models from experimentation to production, operating at the intersection of applied machine learning, model training and evaluation, AI platform engineering, and operational AI deployment. Responsibilities include writing training pipelines, integrating models into containerized deployments, and briefing stakeholders on evaluation results.

Requirements

3 to 6 years of hands-on experience building and shipping production software or AI/ML systems
Strong Python software engineering skills; writes clean, maintainable, production-quality code rather than notebook-only scripts
Demonstrated experience developing and evaluating machine learning models, with a clear understanding of what makes an evaluation rigorous versus misleading
Hands-on familiarity with modern ML frameworks such as PyTorch, TensorFlow, JAX, or Hugging Face
Experience building and managing model training pipelines and experimentation workflows at a level beyond tutorial projects
Experience working with distributed systems or cloud-native environments; comfortable in infrastructure that isn’t fully managed for you
Strong debugging instincts; able to diagnose failure modes in complex pipelines and explain findings clearly to both technical and non-technical audiences
Ability to work independently and manage workstreams without close supervision while staying well-integrated with a distributed team
Strong written and verbal communication skills; able to produce clear technical documentation, status updates, and evaluation summaries
Ability to obtain Security+ certification within the first 90 days of employment
U.S. citizenship required; ability to obtain and maintain a Top Secret/SCI clearance

Nice To Haves

Experience fine-tuning foundation models, LLMs, or multimodal models for specific domain tasks or constrained operational environments
Experience designing or operating model evaluation frameworks and benchmarking pipelines at scale
Experience with Kubernetes and containerized ML workloads, including deploying and debugging GPU-enabled inference services
Experience with distributed training or large-scale inference systems
Familiarity with streaming or event-driven architectures such as Kafka or Flink, particularly as they relate to real-time model inputs or outputs
Experience building secure, compliant AI systems for regulated or mission-critical environments, including familiarity with RMF or IL requirements
Prior defense, national security, or government R&D experience, particularly with AFRL or Air Force programs
Experience working in prototype-to-production environments where research artifacts need to become operational systems
Active Secret or Top Secret clearance strongly preferred

Responsibilities

Build and evaluate machine learning models for mission-relevant use cases working directly with government researchers and program stakeholders to understand requirements and translate them into executable technical solutions
Develop and maintain model training, fine-tuning, and benchmarking workflows that are reproducible, well-documented, and usable by teammates without hand-holding
Build and improve evaluation pipelines for repeatable, rigorous performance measurement across model architectures, datasets, and operational scenarios
Integrate models into production-ready [R]AIMS platform infrastructure, working with platform engineers to ensure deployments are containerized, observable, and operationally sustainable
Support experimentation across model architectures and datasets, maintaining clear records of results and surfacing actionable findings to AI leadership and mission stakeholders