AI Engineer, Senior Architect

Clarity Innovations is a trusted national security partner, dedicated to safeguarding our nation’s interests and delivering innovative solutions that empower the Intelligence Community (IC) and Department of Defense (DoD) to transform data into actionable intelligence, ensuring mission success in an evolving world.

Our mission-first software and data engineering platform modernizes data operations, utilizing advanced workflows, CI/CD, and secure DevSecOps practices. We focus on challenges in Information Warfare, Cyber Operations, Operational Security, and Data Structuring, enabling end-to-end solutions that drive operational impact.

We are committed to delivering cutting-edge tools and capabilities that address the most complex national security challenges, empowering our partners to stay ahead of emerging threats and ensuring the success of their critical missions. At Clarity, we are people-focused and set on being a destination employer for top talent, offering an environment where innovation thrives, careers grow, and individuals are valued. Join us as we continue to lead innovation and tackle the most pressing challenges in national security.

We are seeking an AI Engineer to join our internal R&D team, driving innovative research and development initiatives across advanced intelligence technologies while upholding rigorous data-protection standards. The AI Engineer's main duties will include building goal-driven agent frameworks that can reason, plan, and act in complex scenarios; conducting systematic experimentation and benchmarking against robustness, privacy, and cost constraints; and rapidly prototyping demonstration pipelines that can transform into secure, production-ready inference systems.

The AI Engineer will also produce detailed architectural documentation, data-flow diagrams, and research reports that capture experimental insights. Translating high-level mission requirements into concrete research targets will be key, and the engineer will collaborate closely with product, cyber-operations, and policy teams. Throughout the process, the engineer will share findings through internal presentations and technical briefings, driving the end-to-end lifecycle of AI models from concept to rigorous evaluation and documentation.

Core Responsibilities

• Custom Model Design & Development – Craft novel neural architectures, fine‑tune LLMs, or train domain‑specific models on classified‑grade data sets.
• LLM Agent Workflow Innovation – Design and prototype multi‑step, tool‑use agent frameworks capable of reasoning, planning, and acting in high‑stakes environments.
• Experimentation & Benchmarking – Systematically evaluate model performance, robustness, and efficiency under IL‑2 → IL‑6 constraints, including adversarial resilience and privacy‑compliant metrics.
• Proof‑of‑Concept Development – Rapidly ship technical prototypes that demonstrate feasibility for a future baseline product (e.g., a secure, agent‑centric inference pipeline).
• Architecture & Design Documentation – Produce detailed schematics, data‑flow diagrams, and research reports that capture experimental insights.
• Cross‑Team Engagement – Translate mission requirements into concrete research goals, informing product, cyber‑operations, and policy teams.
• Dissemination & Knowledge Sharing – Present findings internally, write technical briefings and paper drafts, and maintain a private research repository.
• Continuous Learning – Stay abreast of cutting‑edge developments (new kernel methods, prompt‑engineering, policy‑as‑code for agents) and inject fresh ideas into the R&D org.

Technical Requirements

• Education – Bachelor’s (or higher) in Computer Science, Electrical Engineering, Mathematics, or related field; Master’s/Ph.D. in AI/ML, Data Science, or Security strongly preferred.
• Machine Learning – In‑depth knowledge of transformer, diffusion, multi‑modal, and other modern architectures; experience training or fine‑tuning large‑scale models.
• Data Science & Feature Engineering – Ability to engineer domain‑specific features, preprocess encrypted/sensitive data, and build statistically sound training pipelines.
• LLM Agent Design – Proven experience building goal‑driven agent frameworks and multi‑tool orchestration pipelines.
• Experimentation & Evaluation – Skilled at crafting rigorous protocols, benchmarking on multiple metrics, and analyzing results.
• Python & Scientific Stack – Expert with PyTorch, TensorFlow, Hugging Face Transformers, Ray, Dask, and related libraries.
• Model Lifecycle & Governance – Understanding of model provenance, versioning, and lifecycle policy aligned with regulatory constraints (FedRAMP, DoD IL controls).
• Observability & Profiling – Experience using profiling tools (Nsight, PyTorch Profiler) and cost‑tracking for GPU/LLM workloads.
• Policy & Compliance – Familiarity with RBAC/ABAC principles, secure software development lifecycle, and policy‑as‑code for regulated environments.
• Cross‑Domain Collaboration – Comfortable translating high‑level intelligence or cyber‑operations requirements into workable research questions and prototypes.

Preferred qualifications

• Prior work on custom LLMs for defense or intelligence applications (domain‑specific models, hierarchical prompting).
• Experience with semi‑automatic or fully automatic agent orchestration using Temporal, Cadence, or proprietary research engines.
• Publications on model architecture innovations, agent workflows, or secure inference.
• Familiarity with model context protocols or related standards for advanced prompting.
• Hands‑on experience with CUDA/CUDNN, NVIDIA TensorRT, or other GPU optimisation techniques.