ZipRecruiter

Log In

1

Ai Labelling Jobs in Virginia (NOW HIRING)

Telus Digital

$168K - $210K/yr

Stay ahead of industry trends in AI training data, including emerging labeling methodologies ... quality assurance approaches, and the evolving regulatory landscape surrounding responsible AI, and ...

Cloudera

AI Solutions Architect

Virginia Beach, VA · Remote

As an AI Solutions Engineer within Cloudera's Public Sector Consulting team, you will be the ... labels. * Experience installing and operating Cloudera Data Platform * Experience installing and ...

Judge Group, Inc.

Sr. Data Engineer (AI/ML)

Reston, VA · Remote

$100K - $160K/yr

Experience with ETL, Data Labeling and Data Prep. Experience designing, implementing, and maintaining data architecture and services to be used for AI/ML. Additionally, operationalizing and ...

eSolutionsFirst, LLC

.Net Architect With AI

Vienna, VA

$63.75 - $83/hr

.Net Architect With AI 6 Months contract - Extension long term Contract Location: Vienna, VA ... Familiarity with Teklynx Sentinel, Zebra label printing APIs, or industrial hardware integration

next page

Showing results 1-20

All JobsAi Labelling JobsAi Labelling Jobs in Virginia

Ai Labelling information

What are some typical challenges faced in AI Labelling roles and how can they be managed?

One common challenge in AI Labelling roles is maintaining accuracy and consistency when labeling large volumes of data according to detailed guidelines, which can become repetitive or mentally taxing. Managing these challenges often involves taking regular breaks, double-checking work, and staying up-to-date with any updates to annotation standards provided by the team. Collaborating with supervisors and peers to clarify uncertainties and seek feedback also helps ensure high-quality output. Over time, professionals in this role often develop efficient workflows and a keen eye for detail, opening doors to advancement into quality assurance or project coordination positions within the data annotation field.

What is an AI Labelling job?

An AI labelling job involves annotating data—such as images, text, audio, or video—to help train machine learning models. This process includes tasks like tagging objects in images, transcribing speech, or categorizing text. The labelled data is crucial for AI systems to learn and make accurate predictions. These jobs are commonly found in industries like tech, healthcare, and autonomous driving. Attention to detail and consistency are key skills for this role.

What are the key skills and qualifications needed to thrive in the Ai Labelling position, and why are they important?

To thrive in an AI Labelling role, you need attention to detail, basic data analysis skills, and the ability to follow complex guidelines, with many roles requiring at least a high school diploma or equivalent. Familiarity with data annotation tools, image or text labeling platforms, and sometimes basic scripting or database systems is beneficial. Strong communication, time management, and the ability to work both independently and as part of a team are valuable soft skills. These competencies ensure the consistent and accurate labeling of data, which is critical for training high-quality AI and machine learning models.

What are popular job titles related to Ai Labelling jobs in Virginia? For Ai Labelling jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Ai Labelling jobs in Virginia look for? The top searched job categories for Ai Labelling jobs in Virginia are:
What cities in Virginia are hiring for Ai Labelling jobs? Cities in Virginia with the most Ai Labelling job openings:
Ai Labelling jobs near you

Senior RF Machine Learning Engineer

Quartermaster AI Inc

Arlington, VA • On-site

Full-time

Posted 28 days ago

Job description

About Us:
At Quartermaster AI, we believe the ocean should be a safe and sustainably managed resource for all. By leveraging cutting-edge AI and robotics, we unlock capabilities that were only recently impossible. Our distributed open-ocean systems enable every vessel to sense, compute, and communicate, enhancing maritime domain awareness for those who need it most.
Job Description:
Quartermaster AI is seeking a Senior AI/ML Engineer with an emphasis in RF analysis to develop and deploy machine learning systems that utilize RF data for real-time maritime intelligence.
You'll work in a small team of experienced engineers to build detection, classification, and tagging models that help provide contextual understanding of vessel activity based on observed RF signatures.
Key Responsibilities:
  • Design, train, and deploy machine learning models for RF signal detection, classification, and vessel activity tracking.
  • Build and maintain dataset curation pipelines, including AIS-correlated ground truth labeling, synthetic RF data generation, and augmentation strategies for class-imbalanced maritime environments.
  • Build the interface between DSP feature outputs and model inputs by defining pre-processing, normalization, and feature extraction requirements in coordination with the DSP engineer.
  • Develop model evaluation frameworks and benchmarking harnesses; define quantitative performance criteria and drive iterative improvement against them.
  • Optimize models and inference workflows for deployment on edge compute hardware.
  • Document model architecture, training methodology, dataset provenance, and validation results.
Qualifications (Preferred):
  • Master's or PhD in Machine Learning, Signal Processing, or a closely related field - or equivalent demonstrated experience.
  • 5+ years building and deploying ML systems with a focus on RF or signals data.
  • Proficiency in Python and deep learning frameworks; familiarity with RF-native tooling such as Torchsig is a strong plus.
  • Strong understanding of signal alignment, temporal synchronization, and feature extraction from IQ and spectral data.
  • Proven ability to ship production models, not just research prototypes.
  • Experience in maritime, aerospace, or operationally demanding spectral environments.
  • Experience building labeled RF datasets from ground truth sources.
  • Familiarity with edge inference constraints and optimization techniques (quantization, pruning, model distillation).
  • Active Secret clearance or demonstrated ability to obtain one.

Apply