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Remote Rf Filter Design Engineer Jobs in Washington, DC

Spire

Senior Solutions Architect

Washington, DC · Remote

... remote sensing, you have a clear grasp of mission design trades, RF spectrum considerations, and ... Bachelor's degree in relevant Engineering (e.g. Aerospace, Mechanical, Electrical, Systems ...

Spire

Senior Solutions Architect

Washington, DC · Remote

... remote sensing, you have a clear grasp of mission design trades, RF spectrum considerations, and ... Bachelor's degree in relevant Engineering (e.g. Aerospace, Mechanical, Electrical, Systems ...

Spire

Senior Solutions Architect

Washington, DC · Remote

... remote sensing, you have a clear grasp of mission design trades, RF spectrum considerations, and ... Bachelor's degree in relevant Engineering (e.g. Aerospace, Mechanical, Electrical, Systems ...

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Remote Rf Filter Design Engineer information

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$41.9K

$133.3K

$207.3K

How much do remote rf filter design engineer jobs pay per year?

As of May 28, 2026, the average yearly pay for remote rf filter design engineer in Washington, DC is $133,284.00, according to ZipRecruiter salary data. Most workers in this role earn between $110,400.00 and $157,400.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Remote RF Filter Design Engineer, and why are they important?

To thrive as a Remote RF Filter Design Engineer, you need a solid background in electrical engineering, signal processing, and RF circuit design, typically supported by a relevant degree and experience in designing RF/microwave filters. Proficiency with design and simulation tools such as ADS, HFSS, CST, or Microwave Office, along with familiarity with PCB layout software, is essential. Strong problem-solving skills, attention to detail, and effective remote communication are crucial soft skills for collaborating with distributed teams and managing complex projects. These skills and qualities ensure precise, efficient filter designs that meet performance specifications and enable seamless teamwork in a remote environment.

What are some common challenges faced by Remote RF Filter Design Engineers, and how can they effectively collaborate with cross-functional teams?

Remote RF Filter Design Engineers often face challenges related to clear communication and coordination with hardware, PCB layout, and manufacturing teams who may be located in different time zones. To overcome these, it's important to leverage collaboration tools, maintain thorough documentation, and schedule regular virtual meetings to review design specifications, test results, and project milestones. Building strong relationships with team members and proactively addressing technical concerns can help ensure that filter designs meet both performance requirements and manufacturing constraints.

What is a Remote RF Filter Design Engineer?

A Remote RF Filter Design Engineer is a professional who specializes in designing radio frequency (RF) filters used in electronic communication devices, such as smartphones, radios, and wireless systems. They work remotely, using specialized software and tools to create, simulate, and optimize RF filter circuits that block unwanted frequencies and allow desired signals to pass. The role requires a strong background in electrical engineering, RF principles, and experience with simulation tools. Remote RF Filter Design Engineers often collaborate with teams online and may work for companies in telecommunications, defense, or consumer electronics industries.

What is the difference between Remote Rf Filter Design Engineer vs Remote RF Circuit Design Engineer?

AspectRemote Rf Filter Design EngineerRemote RF Circuit Design Engineer
Primary FocusDesigning RF filters to select or reject specific frequency bandsDesigning RF circuits including amplifiers, mixers, and oscillators
Skills & CertificationsRF filter theory, simulation tools, RF component knowledgeRF circuit design, PCB layout, RF simulation tools
Work EnvironmentElectronics or telecommunications companies, remote teamsWireless device manufacturers, remote engineering teams

The main difference is that Remote Rf Filter Design Engineers specialize in creating RF filters for signal processing, while Remote RF Circuit Design Engineers work on broader RF circuit components. Both roles require RF design expertise and often overlap, but their focus areas differ within the RF engineering field.

What are the most commonly searched types of Rf Filter Design Engineer jobs in Washington, DC? The most popular types of Rf Filter Design Engineer jobs in Washington, DC are:
What are popular job titles related to Remote Rf Filter Design Engineer jobs in Washington, DC? For Remote Rf Filter Design Engineer jobs in Washington, DC, the most frequently searched job titles are:
What job categories do people searching Remote Rf Filter Design Engineer jobs in Washington, DC look for? The top searched job categories for Remote Rf Filter Design Engineer jobs in Washington, DC are:
Remote Rf Filter Design Engineer jobs near you
AI/ML Engineer, Senior - WFH1650

AI/ML Engineer, Senior - WFH1650

Global InfoTek, Inc.

Reston, VA • On-site, Remote

$108.70K - $149.30K/yr

Full-time

Posted 5 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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