ZipRecruiter

Log In

1

Feature Producer Jobs in Virginia (NOW HIRING)

Venturegloballng

Engineer, Machine Learning

Arlington, VA · On-site

$157K - $185K/yr

Venture Global LNG ("Venture Global") is a long-term, low-cost provider of American-produced ... Improve model accuracy through feature engineering, tuning, and observability. * Improve model ...

next page

Showing results 1-20

All JobsFeature Producer JobsFeature Producer Jobs in Virginia

Feature Producer information

See Virginia salary details

$39.2K

$104.3K

$166.6K

How much do feature producer jobs pay per year?

As of Aug 19, 2026, the average yearly pay for feature producer in Virginia is $104,307.00, according to ZipRecruiter salary data. Most workers in this role earn between $70,900.00 and $140,300.00 per year, depending on experience, location, and employer.

What is a feature producer?

A Feature Producer is responsible for developing, planning, and executing feature segments for television, film, or digital media productions. They research and pitch story ideas, coordinate interviews, oversee scripting, and ensure that the final product aligns with the creative vision. Working closely with directors, editors, and production teams, they manage budgets, schedules, and logistics to deliver high-quality content. Strong storytelling, organizational, and communication skills are essential for this role.

What are the key skills and qualifications needed to thrive as a feature producer?

To thrive as a Feature Producer, you need expertise in storytelling, project management, and editorial judgment, typically backed by experience in journalism, film, or broadcast media. Familiarity with video editing software (like Adobe Premiere or Avid), production scheduling tools, and content management systems is often required. Strong collaboration, communication, and problem-solving skills help you coordinate with creative and technical teams. These abilities are essential to efficiently manage the production process, meet deadlines, and deliver compelling features that engage audiences.

What are the typical challenges a feature producer might encounter in this role?

Feature Producers often face tight deadlines and the need to balance creative vision with logistical constraints, such as budgets and resources. Coordinating among various departments—like writers, editors, camera crews, and post-production teams—can require agile communication and effective problem-solving skills. Adapting to last-minute changes or shifts in project priorities is also common. However, these challenges are part of what makes the role dynamic, and overcoming them can lead to highly rewarding, visible contributions to the final product.

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 Feature Producer jobs in Virginia?

For Feature Producer jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Feature Producer jobs in Virginia look for?

The top searched job categories for Feature Producer jobs in Virginia are:

Feature Producer jobs near you
Infographic showing various Feature Producer job openings in Virginia as of August 2026, with employment types broken down into 78% Full Time, 18% Part Time, and 4% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $104,307 per year, or $50.1 per hour.

AI/ML Engineer, Senior - WFH1659

Global InfoTek Inc

Reston, VA • On-site

$150 - $200/hr

Temporary

Re-posted 18 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.