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 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 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

What Success Looks Like:

• Models developed and evaluated at AFRL are delivered with clear, rigorous documentation of performance, limitations, and operational considerations-not handed off as black boxes
• Evaluation pipelines are repeatable and trusted by the broader team; results are reproducible and traceable
• Model integrations into [R]AIMS are clean, containerized, and maintainable by platform engineers without needing the original model developer in the loop
• AFRL stakeholders view Raft as a technically credible, reliable partner; your presence in Rome strengthens that relationship over time
• The gap between experimentation and operational deployment shortens with each program cycle because of the infrastructure and workflows you helped build

Clearance Requirements:

• No clearance required to start
• Must be eligible for and willing to obtain a Top Secret/SCI clearance; active clearance strongly preferred

Salary Range: $170,000.00 - $220,000.00

Work Type:

• Hybrid in Rome, NY; candidates must be based in or willing to relocate to the Rome, NY area to support a hybrid schedule
• Up to 25% travel

What we will offer you: 

• Highly competitive salary
• Fully covered healthcare, dental, and vision coverage
• 401(k) and company match
• Take as you need PTO 11 paid holidays
• Education & training benefits
• Generous Referral Bonuses
• And More!

Our Vision Statement: 

We bridge the gap between humans and data through radical transparency and our obsession with the mission. 

Our Customer Obsession:

We will approach every deliverable like it's a product. We will adopt a customer-obsessed mentality. As we grow, and our footprint becomes larger, teams and employees will treat each other not only as teammates but customers. We must live the customer-obsessed mindset, always. This will help us scale and it will translate to the interactions that our Rafters have with their clients and other product teams that they integrate with. Our culture will enable our success and set us apart from other companies.

How do we get there?

Public-sector modernization is critical for us to live in a better world. We, at Raft, want to innovate and solve complex problems. And, if we are successful, our generation and the ones that follow us will live in a delightful, efficient, and accessible world where out-of-box thinking, and collaboration is a norm. 

Raft’s core philosophy is Ubuntu: I Am, Because We are. We support our “nadi” by elevating the other Rafters. We work as a hyper collaborative team where each team member brings a unique perspective, adding value that did not exist before. People make Raft special. We celebrate each other and our cognitive and cultural diversity. We are devoted to our practice of innovation and collaboration.