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We are looking for an applied engineer to own our radar and ML technical stack. This is a blended role at the intersection of radar/SAR simulation, machine learning, scientific software, and compute infrastructure.

The ideal candidate is not a pure data scientist or a pure signal processing engineer. They are a technical owner who can move across the stack: from radar simulations and data preprocessing to ML model development, local GPU/compute setup, and hand-offs of radar products to software and hardware teams.

Clearance: Active TS/SCI clearance. US Citizenship is required.

Location: Chantilly, VA (Monday-Thursday Onsite & Friday Remote)

Responsibilities: 

• Act as a technical liaison, fostering effective communication and collaboration between radar engineering, machine learning, software engineering, and operation teams. 
• Develop and maintain robust radar and SAR simulation pipelines, including synthetic data generation, scene/return modeling, and validation workflows.
• Design, build and refine end-to-end ML models and pipelines for radar-related tasks, including preprocessing, training, evaluation, and deployment-ready packaging.
• Utilize and analyze defense-focused datasets, including radar, 3D models, Electro-Optical/Infrared (EO/IR), and sensing-adjacent data.
• Create radar products and technical deliverables for internal software teams and hardware partners, including APIs, data schemas, containers, documentation, and integration guidance.
• Design, configure, and optimize local compute environments, including GPU/eGPU setups, remote compute, storage, networking, containerization, and benchmarking.
• Support ML inference/training on constrained or embedded compute, with awareness of systems such as RFSoCs, FPGAs, and related hardware constraints.
• Collaborate with RF/hardware partners to support internal RF code processing, radar outputs, and productization of deployable radar hardware
• Help deploy and maintain web applications and internal tools on classified or restricted networks.
• Contribute to technical writing, SBIR proposals, and system documentation.

 Required Qualifications: 

• Deep experience in Synthetic Aperture Radar, non-imaging radar, remote sensing, or signal processing
• Solid understanding of radar/SAR fundamentals, including:
• I/Q and complex-valued data
• Simulation techniques
• Image formation algorithms and radar-to-image pipelines
• Coherent vs. incoherent processing
• Proven track record of experience with radar or remote sensing simulations
• Strong proficiency with scientific Python libraries:
• NumPy, PyTorch, SciPy, Matplotlib, Jupyter, and related scientific stacks
• Demonstrated ability to build end- to-end ML pipelines encompassing:
• Data preprocessing
• Training
• Evaluation
• Versioning
• Packaging
• Hand-off to other engineers
• Hands-on experience with GPU compute, such as:
• PyTorch
• CUDA
• NVIDIA tooling
• Remote GPU Servers
• Local GPU compute
• Ability to explain radar/ML concepts to non-radar engineers and produce clear technical deliverables
• Adherence to robust software engineering principles and best practices (e.g. clean code, testing, version control).
• Exceptional communication skills, with the ability to clearly articulate complex radar and ML concepts to both technical and non-technical audiences, and to produce high-quality technical documentation and deliverables.

 Preferred Skills/Experience:

• Experience with Xpatch simulations specifically
• Experience with CAD and or artistic 3D modeling skills
• Experience with EO/IR or multi-sensor fusion
• Experience with adversarial imaging AI
• Understanding of RFSoCs, FPGAs, HLS, quantization, or edge deployment constraints
• Experience designing or optimizing local compute servers / GPU clusters / eGPU configurations
• Experience working with RF hardware partners or hardware-in-the-loop systems
• Experience with LLMs, LoRA fine-tuning, or local model deployment for niche tasks
• Experience with container computing and orchestration

Physical Requirements: 

• Prolonged periods sitting at a desk and working on a computer 
• Must be able to lift up to 10-15 pounds at time 

#JT