Senior Machine Learning Engineer, Radar & Remote Sensing

Working at NT Concepts means that you are part of an innovative, agile company dedicated to solving the most critical challenges in National Security. We’re looking for the best and the brightest to join us in supporting this mission. If meaningful work, initiative, creativity, and continuous self-improvement are important to your career, join our growing team and discover What's Next for you. We tackle hard problems to meet our clients' needs.

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