AI/ML Engineer
Role details
Job location
Tech stack
Job description
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 Air Force Research Laboratory (AFRL) and are looking for a hands-on AI/ML Engineer to contribute directly to model development, evaluation, and operational AI delivery., 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, * 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, For government reporting purposes, we ask candidates to respond to the below self-identification survey. Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiring process or thereafter. Any information that you do provide will be recorded and maintained in a confidential file.
As set forth in Raft Company Website's Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.
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If you believe you belong to any of the categories of protected veterans listed below, please indicate by making the appropriate selection. As a government contractor subject to the Vietnam Era Veterans Readjustment Assistance Act (VEVRAA), we request this information in order to measure the effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. Classification of protected categories is as follows:
A "disabled veteran" is one of the following: a veteran of the U.S. military, ground, naval or air service who is entitled to compensation (or who but for the receipt of military retired pay would be entitled to compensation) under laws administered by the Secretary of Veterans Affairs; or a person who was discharged or released from active duty because of a service-connected disability.
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An "Armed forces service medal veteran" means a veteran who, while serving on active duty in the U.S. military, ground, naval or air service, participated in a United States military operation for which an Armed Forces service medal was awarded pursuant to Executive Order 12985.
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
- 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
Benefits & conditions
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.