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Remote Radar Signal Processing Engineer Jobs in Washington

Spear AI

Machine Learning Engineer

Washington, DC · On-site +1

Work with signal processing data and time-series analysis * Improve local development and CI/CD for ... Onsite / Remote / Flexible work arrangements or hybrid options (position dependent) * Relocation ...

Expression

AI/ML Engineer

Washington, DC · On-site +1

$130K - $170K/yr

Experience applying AI to sensor analytics, time-series data, or signal processing. * Experience ... Hybrid or Remote with limited travel Benefits: Expression offers competitive salaries and benefits ...

iDirect GOVERNMENT

Senior FPGA Engineer

Herndon, VA · On-site +1

$91K - $159K/yr

This position is based out of our Herndon, VA location with the option of a remote work schedule ... Experience in Digital Signal Processing on FPGAs and SDR waveform implementation * Experience in ...

GRVTY

Software Engineer, Senior

Herndon, VA · On-site +1

$126K - $166K/yr

What Impact You'll Have GRVTY is looking for a Senior Software Engineer to join a small ... Background in signal processing, image processing, or remote sensing data workflows. * Experience ...

Amyx, Inc.

Counter UAS Analysis Team Lead

Washington, DC · On-site +1

$18 - $23.75/hr

Responsibilities * Lead and manage a team of analysts and engineers focused on C-UAS threat ... Desired: * Experience with RF spectrum analysis, radar systems, and signal processing.

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Remote Radar Signal Processing Engineer information

What are the key skills and qualifications needed to thrive as a remote radar signal processing engineer, and why are they important?

To thrive as a Remote Radar Signal Processing Engineer, you need a strong background in electrical engineering, digital signal processing, and radar theory, typically supported by a relevant degree. Proficiency with MATLAB, Python, C/C++, and tools like Simulink or FPGA development environments is often required, along with familiarity with simulation and analysis software. Excellent problem-solving, communication, and teamwork skills are crucial for collaborating with remote teams and conveying technical concepts clearly. These competencies are essential for developing effective radar solutions, ensuring system reliability, and supporting innovation in distributed engineering environments.

What are some common challenges faced by remote radar signal processing engineers and how can they be addressed?

Remote Radar Signal Processing Engineers often encounter challenges such as managing large volumes of complex data, ensuring reliable communication with distributed teams, and debugging system performance without direct access to hardware. To address these challenges, engineers typically rely on advanced simulation tools, maintain clear and regular communication with hardware teams, and use remote desktop solutions to access necessary systems. Emphasizing strong documentation and version control also helps keep projects organized and collaborative in a remote setting.

What is a remote radar signal processing engineer?

Remote Radar Signal Processing Engineers are professionals who design, develop, and optimize algorithms and systems for processing radar signals, often while working remotely. They analyze raw radar data to extract meaningful information, such as object detection, tracking, and imaging. These engineers use advanced mathematical models and programming to enhance radar system performance and reliability. Their work is crucial in applications like defense, aviation, weather forecasting, and autonomous vehicles.

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Remote Radar Signal Processing Engineer jobs near you
Infographic showing various Remote Radar Signal Processing Engineer job openings in Washington as of August 2026, with employment types broken down into 80% Full Time, 15% Part Time, 1% Temporary, 3% Contract, and 1% Nights. Highlights an 92% Physical, 3% Hybrid, and 5% Remote job distribution.

AI/ML Engineer, Senior - WFH1659

Global InfoTek, Inc.

Reston, VA • On-site, Remote

$150 - $200/hr

Full-time

Re-posted 14 days ago

Job description

Clearance Level: Public Trust
US Citizenship: Required
Job Classification: 1099/Contractor ($150 - $200 per hour)
Location: Remote
Years of Experience: 5-7 years of relevant experience
Education Level: BS or MS in Electrical Engineering, Computer Science, Applied Mathematics, or a closely related quantitative field. Experience may be considered in place of education requirement.
Briefly Describe the Work:
GITI is seeking a Senior AI/ML Engineer to support an R&D program focused on passive RF emitter identification and network analysis from real-time sensor data streams. The Senior AI/ML Engineer designs, builds, and validates machine learning models for RF emitter identification, conducts hands-on exploratory data analysis on NDF (Network Description File) sensor datasets, and implements ML data pipelines that operate on constrained tactical edge hardware. Working under the direction of the Principal AI/ML Engineer and program technical lead, the candidate collaborates closely with research scientists and software engineers to translate analytical findings into reproducible, well-documented ML experiments and pipeline components. The role requires strong Python and deep learning skills, comfort with real-world noisy sensor data, and the ability to work in air-gapped Linux environments without cloud infrastructure or GPU acceleration.
Responsibilities:
  • Design, build, and validate machine learning models for RF emitter identification - including feature engineering from sensor data, training pipeline development, model evaluation, and iterative refinement based on results
  • Conduct hands-on exploratory data analysis on RF sensor datasets using Python and Jupyter notebooks - writing and running analytical code, characterizing feature distributions, identifying data quality issues, and producing documented findings
  • Implement and maintain ML data pipelines - ingesting NDF sensor streams, applying rollup and preprocessing logic, constructing training datasets, and ensuring pipeline correctness on constrained edge hardware with no cloud dependency
  • Collaborate with the technical lead and Principal AI/ML Engineer to investigate RF sensor data quality, attribution reliability, and feature behavior under contention - writing code to characterize error sources, validate assumptions, and reproduce findings
  • Produce clear technical documentation of experiments, model configurations, and results - maintaining reproducibility through disciplined versioning, and contributing to monthly status reports and team knowledge sharing

Career level with a complete understanding and wide application of machine learning principles and data science techniques. Working under general direction from the Principal AI/ML Engineer, executes independently on assigned modeling and analysis tasks, contributes to pipeline development, and produces reproducible, well-documented results. Bachelor's or Master's (or equivalent) with 5-7 years of hands-on applied experience.
Required Skills:
  • 5+ years of hands-on applied experience in machine learning, data science, or RF signal processing
  • Demonstrated proficiency in Python for ML and data science work - PyTorch or TensorFlow for model development, Pandas/NumPy for data manipulation, and scikit-learn or similar for evaluation and baseline modeling
  • Hands-on experience designing, training, and evaluating deep learning models - particularly metric learning, Siamese networks, or other similarity-learning architectures - on real-world, noisy, imbalanced datasets
  • Practical experience handling real-world data quality problems - missing values, label noise, class imbalance, systematic bias, and sensor artifacts - and the ability to diagnose and address them without discarding valid data
  • Ability to develop and run ML pipelines on Linux-based systems without cloud infrastructure or GPU acceleration - optimizing for CPU-only inference and multi-threaded data processing on resource-constrained x86 hardware

Desired Skills:
  • Familiarity with RF signal characteristics, passive receiver phenomenology, and sensor data interpretation - including awareness of processing artifacts, attribution ambiguities, and measurement limits common in signals intelligence datasets
  • Hands-on experience applying machine learning - particularly metric learning, deep learning networks, or similarity-learning architectures - to RF or time-series signal data, including feature engineering, training pipeline development, and model validation
  • Exposure to TDMA network protocols or military datalink systems, and interest in learning the signal processing challenges of dense, contested electromagnetic environments
  • Familiarity with direction-finding, time-difference-of-arrival (TDOA), or related passive geolocation concepts - understanding of their mathematical foundations and common failure modes is more important than operational experience
  • Experience with binary serialization formats (FlatBuffers, Protocol Buffers) and high-throughput sensor data pipelines operating in near-real-time on resource-constrained hardware
  • Background in statistical signal processing - error ellipses, bearing estimation uncertainty, feature reliability under noise - with the ability to distinguish statistically significant findings from artifacts of small sample size or improper normalization

Relevant Certifications:
  • Certifications in machine learning, data science, or related technical fields (e.g., TensorFlow Developer Certificate; PyTorch Certified Associate; AWS Certified Machine Learning - Specialty; Microsoft Certified: Azure AI Engineer Associate; Certified Analytics Professional (CAP); etc.)

Global InfoTek, Inc. is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability.
About Global InfoTek, Inc. Global InfoTek Inc. has an award-winning track record of designing, developing, and deploying best-of-breed technologies that address the nation's pressing cyber and advanced technology needs. GITI has rapidly merged pioneering technologies, operational effectiveness, and best business practices for over two decades.

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