Senior Machine Learning Engineer, Radar & Remote Sensing

• Own the radar and ML technical stack across radar/SAR simulation, machine learning, scientific software, and compute infrastructure
• Act as a technical liaison between radar engineering, machine learning, software engineering, and operations teams
• Develop and maintain 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
• Perform preprocessing, training, evaluation, and deployment-ready packaging
• Utilize and analyze radar, 3D model, EO/IR, and sensing‑adjacent defense datasets
• Create radar products and technical deliverables, including APIs, data schemas, containers, documentation, and integration guidance
• Design, configure, and optimize local compute environments, GPU/eGPU setups, remote compute, storage, networking, containerization, and benchmarking
• Support ML inference/training on constrained or embedded compute, including RFSoCs and FPGAs
• Collaborate with RF/hardware partners on RF code processing, radar outputs, and deployable radar hardware productization
• Help deploy and maintain web applications and internal tools on classified or restricted networks
• Contribute to technical writing, SBIR proposals, and system documentation

• Active TS/SCI clearance
• US Citizenship is required
• 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, and coherent vs. incoherent processing
• Proven experience with radar or remote sensing simulations
• Strong proficiency with scientific Python libraries, including Num Py, PyTorch, Sci Py, Matplotlib, Jupyter, and related scientific stacks
• Ability to build end-to-end ML pipelines encompassing data preprocessing, training, evaluation, versioning, packaging, and hand‑off to other engineers
• Hands‑on experience with GPU compute, including PyTorch, CUDA, NVIDIA tooling, remote GPU servers, and local GPU compute
• Ability to explain radar/ML concepts to non‑radar engineers and produce clear technical deliverables
• Adherence to software engineering principles and best practices, including clean code, testing, and version control
• Exceptional communication skills and ability to produce high‑quality technical documentation and deliverables
• Prolonged periods sitting at a desk and working on a computer
• Must be able to lift up to 10–15 pounds at a time

Demonstrates expertise in Synthetic Aperture Radar and machine learning, with a strong ability to develop and maintain radar simulation pipelines and end-to-end ML models. Proficient in scientific Python libraries and GPU compute, with a focus on producing clear technical documentation and deliverables.

• Synthetic Aperture Radar Expertise
• End‑to‑End ML Pipeline Development
• Scientific Python Proficiency
• GPU Compute Experience ATS Optimization Keywords Hard Skills
  • Radar Simulation
  • Machine Learning Models
  • Data Preprocessing
  • Image Formation Algorithms
  • Version Control
  • Testing Best Practices
  • Synthetic Data Generation
  • Scene/Return Modeling
  • Coherent Processing
  • Non‑Imaging Radar
  Soft Skills
  • Exceptional Communication Skills
  • Technical Writing
  Certifications & Qualifications
  • Active TS/SCI Clearance
  • US Citizenship
  Industry Keywords
  • Remote Sensing
  • Signal Processing
  • Radar Fundamentals
  • RF Code Processing
  • Embedded Compute
  • Technical Deliverables
  • SBIR Proposals
  • Compute Infrastructure
  • Data Schemas
  • Integration Guidance
  Tools & Technologies
  • Num Py
  • Py Torch
  • Sci Py
  • Matplotlib
  • Jupyter
  • CUDA
  • NVIDIA Tooling
  • Remote GPU Servers
  • Containerization
  • APIs