Responsibilities
- Design, develop, and optimize efficient and lightweight AI/ML models specifically for edge devices with limited computational power, memory, and energy.
- Implement techniques to ensure real-time performance at the tactical edge.
- Develop and integrate on-device learning and adaptive models that can continuously improve and adapt to changing mission environments without constant cloud connectivity.
- Engineer AI/ML solutions for disconnected or intermittently connected operations, ensuring robustness and functionality even when central network access is unavailable.
- Architect and implement advanced sensor fusion to integrate and make sense of disparate and sensor data streams from various modalities.
- Develop multi-sensor perception systems for object detection, tracking, classification, anomaly detection, and situation awareness in complex, dynamic, and potentially contested environments.
- Apply expertise in processing and fusing data from diverse sensor types including
- Address challenges of data synchronization, misalignment, and conflicting information from multiple sensors to generate a coherent and accurate operational picture.
- Develop AI/ML algorithms that enhance decision-making speed and accuracy for warfighters and commanders.
- Focus on applications that provide a direct mission advantage, such as predictive intelligence, threat detection and identification, autonomous navigation, target recognition, battle damage assessment, and enhanced situational awareness.
- Collaborate directly with mission experts and end-users to iteratively design, test, and refine AI/ML capabilities, ensuring operational relevance and usability.
- Design AI/ML systems that are robust to adversarial attacks and can accommodate the realities of mission sensor data quality and noise, environmental noise, ensuring reliable performance in contested and unpredictable operational settings.
- Implement techniques for explainable AI (XAI) to provide warfighters with transparency and confidence in model predictions, especially for critical decisions.
- Develop methods for model monitoring and health checks at the edge, ensuring sustained performance and alerting to degradation or compromise.
- Ensure AI/ML solutions comply with responsible AI principles and ethical guidelines for military applications.
- Design and implement secure MLOps pipelines for continuous integration, continuous delivery (CI/CD), and lifecycle management of AI/ML models from development to deployment at the mission edge.
- Automate model testing, validation, and deployment processes in highly constrained and secure environments.
- Ensure all AI/ML development and deployment adheres to stringent DoD cybersecurity frameworks and secure coding practices.
- Support the integration of FISMA compliance controls into coding practices incorporating unique edge security considerations.
- Implement Zero Trust architectures for AI/ML solution access and data handling at the edge.
- Integrate AI/ML solutions with existing and legacy DoD tactical systems, command and control (C2) platforms, and communications networks.
- Work to ensure seamless data flow and interoperability with various DoD data sources and fusion centers.
- Contribute to the establishment of tactical data lakes or similar constructs at the edge for local data ingestion and AI/ML processing.
- Bachelor's Degree in Engineering, Computer Science, or related field; equivalent, relevant experience will be considered
- Proficiency in PyTorch, Python, JavaScript/TypeScript
- Open-source LLMs (e.g., Llama, Gemma, Qwen) and VLMs (e.g., Phi4, Qwen-VL) using Huggingface
- Expertise in prompt engineering
- Building RAG pipelines using tools like LangChain or LlamaIndex.
- Hands-on experience with Docker, Kubernetes, Helm; model serving frameworks like vLLM or Triton
- Observability tools such as Weights & Biases
- Vector databases like Qdrant or Milvus.
- Experience deploying models on edge devices
- Experience utilizing hardware acceleration tools like CUDA, ONNX, TensorRT
- Proven track record of designing, training, and deploying lightweight and efficient machine learning models for real-time inference on resource-constrained devices
- Experience with MLOps tools and practices for deploying and managing models in production, especially at the edge
- Familiarity with the Model Context Protocol (MCP) for connecting AI models to external tools and data sources
- Understanding of secure, real-time data access methodologies.
- Extensive hands-on experience with multi-modal sensor data fusion techniques and algorithms.
- Demonstrated ability to work with and process diverse sensor data types (e.g., imagery, video, audio, RF, network logs, structured data).
- Experience with signal processing, computer vision, natural language processing (NLP), or other relevant domains for sensor data interpretation.
- Direct experience designing and implementing solutions DDIL communication environments.
- Familiarity with tactics, techniques, and procedures (TTPs) related to military operations at the tactical edge.
- Understanding of the challenges of data collection, storage, and processing in austere and contested operational environments.
- Deep understanding of unique DoD reference architectures such as CJADC2 and MPEs
- Significant experience as an agile and CI/CD practitioner
- Strong analytical and problem-solving skills.
- Excellent communication and interpersonal skills.
- Ability to work effectively across functional groups to optimize product & service offerings.
- Understands the many aspects of United States Government/Department of Defense programs, including but not limited to program and project management, staffing, engineering, Operations and Maintenance (O&M), quality, logistics, technology, and regulations.
- Demonstrated ability to handle multiple projects simultaneously.
- Familiarity of NIST security guidelines, such as 800-53 and 800-63, and good understanding of security fundamentals, as well as authentication with OAuth, SAML etc.
- Knowledge of Go, Rust, or C++ for edge optimization
- Experience integrating GenAI into full-stack applications
- Handling large, multimodal datasets
- Fine-tuning with LoRA.
- Associate level certification with Google, Azure or AWS cloud platforms
- Active SECRET security clearance preferred or be able to secure DoD Security clearance.
Benefits
- Highlights of our benefits include Health/Dental/Vision, 401(k) match, Paid Time Off, STD/LTD/Life Insurance, Referral Bonuses, professional development reimbursement, and parental leave.
About the company
- Compensation is unique to each candidate and relative to the skills and experience they bring to the position. This does not guarantee a specific salary as compensation is based upon multiple factors such as education, experience, certifications, and other requirements, and may fall outside of the above-stated range.
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