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Senior Feature Producer Jobs in Virginia (NOW HIRING)

Epicor

Senior Product Manager | Prism

Herndon, VA · On-site

$130K - $171K/yr

Built as a platform, not a feature. Prism Core exists to be depended on by every other part of ... produced real usage data, not because a new model looked good in a demo. You own the success ...

Gridiron IT

.NET Developer - Senior

Chantilly, VA · On-site

$56.25 - $74.50/hr

... Produces detailed specifications, writes software code, and conducts unit testing. • Writes ... of feature and technical changes in the application. Qualifications : Required : • 7+ years ...

Verisign

Sr Product Marketing Manager

Reston, VA · Hybrid

$135K - $183K/yr

... producing customer-facing and sales collateral, including whitepapers, webinars, case studies ... Develop and execute comprehensive launch plans for new products and feature enhancements. * Conduct ...

GRVTY

Senior Full Stack Developer

Sterling, VA · On-site

This program helps to produce Mission Management web applications for the Intelligence Community ... Maintain knowledge of feature and technical changes in the application * Apply Agile software ...

CaseGuard

Senior Software Engineer -- C# / WPF

Arlington, VA · On-site

$141K - $186K/yr

They are seeking a Senior Software Engineer to drive the evolution of CaseGuard Studio, a feature ... await, producer-consumer pipelines, and cancellation. • Proven experience profiling and ...

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Senior Feature Producer information

What is the difference between Senior Feature Producer vs Content Producer?

AspectSenior Feature ProducerContent Producer
CredentialsExperience in media production, storytelling, project managementSimilar credentials, often with focus on content creation and editing
Work EnvironmentMedia companies, TV/film production, digital mediaMedia, digital platforms, marketing agencies
Industry UsageUsed for experienced producers leading feature stories or segmentsUsed for creators producing various content types

The main difference is that a Senior Feature Producer typically leads the development of in-depth stories or segments, requiring advanced storytelling and project management skills. A Content Producer may handle a broader range of content creation tasks, often focusing on producing and editing content for various platforms. Both roles require media experience, but the Senior Feature Producer usually has more specialized expertise in feature storytelling and project leadership.

What are the most commonly searched types of Feature Producer jobs in Virginia? The most popular types of Feature Producer jobs in Virginia are:
What are popular job titles related to Senior Feature Producer jobs in Virginia? For Senior Feature Producer jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Senior Feature Producer jobs in Virginia look for? The top searched job categories for Senior Feature Producer jobs in Virginia are:
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Senior Feature Producer jobs near you

AI/ML Engineer, Senior - WFH1659

Global InfoTek Inc

Reston, VA • On-site

$150 - $200/hr

Temporary

Re-posted 9 days ago

Job description

Clearance Level: Public Trust (Secret Eligible)

US Citizenship: Required

Job Classification: 1099/Consultant

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.