AI/ML Engineer

This is a U.S. based position. All of the programs we support require U.S. citizenship to be eligible for employment. All work must be conducted within the continental U.S.

This is a U.S. based position. All of the programs we support require U.S. citizenship to be eligible for employment. All work must be conducted within the continental U.S.

Who we are:

Raft (https://TeamRaft.com) is a customer-obsessed non-traditional defense tech company dedicated to empowering U.S. military and government agencies with cutting-edge AI/ML and data solutions. We are a leader in autonomous data fusion and Agentic AI, with a purposeful focus on Distributed Data Systems, Platforms at Scale, and Complex Application Development. With headquarters in McLean, VA, our range of clients includes innovative federal and public agencies leveraging design thinking, cutting-edge tech stack, and cloud-native ecosystem. We build digital solutions that impact the lives of millions of Americans.

Our flagship AI platform, [R]AIMS (Raft AI Mission System), enables operators and engineers to rapidly build, deploy, evaluate, and govern AI-powered mission workflows across highly dynamic operational environments. We are expanding our AI/ML presence in Rome, NY to support our customers and are looking for a hands-on AI/ML Engineer to contribute directly to model development, evaluation, and operational AI delivery.

About The Role:

As an AI/ML Engineer embedded in Rome, NY, you will work directly on model development efforts while leveraging and extending [R]AIMS platform capabilities to accelerate experimentation, evaluation, deployment, and operational transition. This is a highly hands-on role for an engineer who wants to build real-world AI systems with direct mission impact.

You will work closely with platform engineers, AI leadership, and mission stakeholders to move models from experimentation through production. The work sits at the intersection of applied machine learning, model training and evaluation, AI platform engineering, and operational AI deployment. You will need to be comfortable operating across that full span: writing training pipelines one day, integrating a model into a containerized deployment the next, and briefing a technical stakeholder on evaluation results the day after that.

What you’ll do:

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

What we are looking for:

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

S. citizenship required; ability to obtain and maintain a Top Secret/SCI clearance

Highly Preferred:

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 …