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Remote Rf Drive Test Engineer Jobs in Virginia (NOW HIRING)

ICF

FHIR Engineer - Remote

Reston, VA · On-site +1

You will be expected to design, develop, test, and deploy Java applications that meet the needs of ... Work with test engineering team to assure product quality * Collaborate in a fast-paced Agile ...

Sentara Health

DevOps Engineer - Remote

Virginia Beach, VA · Remote

$80K - $133K/yr

This position is fully remote. Overview Software product development focuses on developing multiple ... test engineering, DevOps, deployment, maintenance, and evolution activities by correcting ...

New

Sentara Health

DevOps Engineer - Remote

Virginia Beach, VA · Remote

$80K - $133K/yr

This position is fully remote. Overview Software product development focuses on developing multiple ... test engineering, DevOps, deployment, maintenance, and evolution activities by correcting ...

New

Scout Space

Satellite Electrical Engineer

Reston, VA · On-site +1

... drive our programs forward. As a Satellite Electrical Engineer , you will focus on design ... Experience writing test procedures for both board and assembly level tests * Strong debugging ...

VMD Corp

DevOps Engineer - Secret (Remote)

Mclean, VA · Remote

$53.25 - $73/hr

... cloud-native technologies to drive operational efficiency and resilience across complex ... Demonstrated experience with implementing Test Driven Development (TDD) Methodologies.

HawkEye 360

Senior Software Engineer

Herndon, VA · On-site +1

$130K - $180K/yr

Satellite clusters and ground segments provide the platform for RF data collection that is ... Experience building automated mission-critical applications and supporting remote hardware.

Arcfield

Lead Edge AI/ML Engineer

Richmond, VA · On-site +1

$101K - $133K/yr

... RF signal classification, IMU drift modeling, anomaly detection, and advanced sensor fusion. The ... For Remote Opportunities), education and certifications as well as Federal Government Contract ...

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All JobsRemote Rf Drive Test Engineer JobsRemote Rf Drive Test Engineer Jobs in Virginia

Remote Rf Drive Test Engineer information

What is the difference between Remote Rf Drive Test Engineer vs Rf Drive Test Engineer?

AspectRemote Rf Drive Test EngineerRf Drive Test Engineer
Work EnvironmentPerforms tests remotely, often using specialized software and virtual toolsConducts on-site drive tests using vehicles and physical equipment
CertificationsTypically requires RF certifications and remote testing experienceRequires RF certifications and hands-on testing skills
Industry UsageUsed in scenarios where physical presence is limited or impracticalCommon in traditional network testing and coverage verification

The main difference between Remote Rf Drive Test Engineer and Rf Drive Test Engineer lies in the testing environment. Remote engineers perform tests virtually, while traditional engineers conduct on-site drive tests. Both roles require RF certifications, but remote roles emphasize virtual tools and software. Understanding these differences helps employers and candidates choose the right position based on work setting and technical requirements.

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Remote Rf Drive Test Engineer jobs near you
AI/ML Engineer, Senior - WFH1650

AI/ML Engineer, Senior - WFH1650

Global InfoTek, Inc.

Reston, VA • On-site, Remote

$108K - $149K/yr

Full-time

Posted 29 days ago

Job description

Clearance Level: Public Trust
US Citizenship: Required
Job Classification: Full Time
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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