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Annotation Labelling Jobs in Ashburn, VA (NOW HIRING)

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Annotation Labelling information

What is annotation labelling?

Annotation labelling is the process of tagging or marking data—such as images, text, or audio—with relevant information or labels. This is an essential step in preparing datasets for machine learning and artificial intelligence models, as it helps algorithms understand and learn from raw data. Annotation labelling can include tasks like identifying objects in photos, transcribing speech, or categorizing text. Skilled annotators ensure accuracy and consistency to improve model performance. People in this role often use specialized tools or software to streamline and standardize the annotation process.

What are the key skills and qualifications needed to thrive as an annotation labelling specialist?

To thrive as an Annotation Labelling Specialist, you need strong attention to detail, data analysis capabilities, and familiarity with data annotation standards, usually supported by a background in computer science or related fields. Proficiency with annotation tools such as Labelbox, CVAT, or Supervisely, and sometimes knowledge of basic programming or scripting, is typically required. Excellent communication, consistency, and the ability to follow complex instructions are crucial soft skills for producing high-quality labeled data. These skills ensure the accuracy and reliability of datasets, which are foundational for successful machine learning and AI model development.

What are some common challenges faced by annotation labelling professionals, and how can they be managed?

Annotation Labelling professionals often encounter challenges such as maintaining high accuracy while handling repetitive data, meeting tight deadlines, and adapting to evolving project guidelines. To manage these, it’s important to develop strong attention to detail, regularly communicate with team leads to clarify instructions, and leverage annotation tools efficiently. Collaborating closely with quality assurance teams can also help identify and correct errors early, ensuring consistently high-quality outputs.

What is the difference between Annotation Labelling vs Data Labeling Specialist?

AspectAnnotation LabellingData Labeling Specialist
CredentialsBasic technical skills, attention to detailSimilar skills, sometimes additional domain knowledge
Work EnvironmentData annotation platforms, remote or officeData annotation tasks, often remote or in-office
Industry UsageAI, machine learning, autonomous vehiclesAI, machine learning, healthcare, retail
Search & ComparisonCommonly compared for entry-level data tasksRelated but broader role

Annotation Labelling involves marking data such as images, text, or videos to train AI models. Data Labeling Specialists perform similar tasks but may have a broader scope, including verifying and managing labeled data. Both roles are essential in AI development, often overlapping in skills and work environment, but Annotation Labelling is more focused on the annotation process itself.

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

Machine Learning Engineer, Detection and Tracking

Helsing

Washington, DC • On-site

Full-time

Medical, PTO

Re-posted 24 days ago

Job description

Who we are
Helsing develops artificial intelligence-enabled capabilities to protect and defend democracies. We build Altra, an AI-powered drone software platform, and HX-2, our autonomous drone. We are growing our US operations, cultivating an ambitious and committed team of mission-driven professionals to apply their skills to solve challenging problems.
The role
You will own the detection and tracking models that power Helsing's products - training, tuning, and deploying models against US-specific datasets. This is an applied ML role: you won't be writing research papers, but you will be expected to have strong intuition for model performance, data quality, and the practical trade-offs involved in getting detection and tracking systems to work reliably in production. You will manage the full model lifecycle - from assessing and curating training data through annotation, training, evaluation, and deployment to edge platforms.
The day-to-day
  • Training and fine-tuning detection models (YOLO, DETR, Faster R-CNN, and similar architectures) on mission-specific datasets
  • Implementing and improving multi-object tracking pipelines (SORT, DeepSORT, ByteTrack, or similar)
  • Evaluating model performance: analyzing metrics, diagnosing failure modes, and iterating on data and model improvements
  • Managing the data pipeline end-to-end: assessing raw data, coordinating annotation, curating datasets, and implementing augmentation strategies
  • Optimizing models for deployment on SWaP-constrained and embedded platforms (quantization, pruning, TensorRT, ONNX export)
  • Collaborating with systems engineers to integrate models into the broader Altra platform
  • Working across sensor modalities as needed, including electro-optical, infrared, and other imaging sources
You should apply if you
  • Have 5+ years of experience in applied machine learning or computer vision
  • Have a Bachelor's degree in Computer Science, Electrical Engineering, or a related field; Master's or PhD strongly preferred
  • Have production experience training and deploying object detection models - not just research or academic projects
  • Are proficient in Python and PyTorch or a comparable deep learning framework
  • Have strong intuition for data quality; you can look at annotated datasets, training curves, and evaluation metrics and know what's wrong
  • Have experience with the full model training lifecycle: data curation, annotation management, training, evaluation, and deployment
  • Have experience optimizing models for deployment on SWaP-constrained and edge platforms (TensorRT, ONNX, quantization)
  • Understand multi-object tracking and have implemented or worked with tracking algorithms in practice
  • Can read and contextualize scientific papers in computer vision and apply findings to production systems
  • Are a U.S. citizen with an active security clearance or the ability to obtain one
Nice to have
  • Strong proficiency in Rust or C++ for production model deployment and optimization
  • Experience with multiple sensor modalities - particularly infrared or thermal imaging
  • Familiarity with MLOps tooling: experiment tracking (MLflow, Weights & Biases), dataset versioning, model registries
  • Experience with annotation tools and workflows (CVAT, Label Studio, or similar)
  • Background in computer vision beyond detection - segmentation, pose estimation, activity recognition
  • Experience with simulators, emulators, or synthetic data generation for training and evaluation
  • Experience deploying models on GPU-accelerated embedded platforms (NVIDIA Jetson, similar)
  • Background in defense, intelligence, or other mission-critical environments
Join Helsing and work with world-leading experts in their fields
  • Helsing's work is important. You'll be directly contributing to the protection of democratic countries while balancing both ethical and geopolitical concerns
  • The work is unique. We operate in a domain that has highly unusual technical requirements and constraints, and where robustness, safety, and ethical considerations are vital. You will face unique Engineering and AI challenges that make a meaningful impact in the world
  • Our work frequently takes us right up to the state of the art in technical innovation, be it reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The defense industry is entering the most exciting phase of the technological development curve. Advances in our field of world are not incremental: Helsing is part of, and often leading, historic leaps forward
  • In our domain, success is a matter of order-of-magnitude improvements and novel capabilities. This means we take bets, aim high, and focus on big opportunities. Despite being a relatively young company, Helsing has already been selected for multiple significant government contracts
  • We actively encourage healthy, proactive, and diverse debate internally about what we do and how we choose to do it. Teams and individual engineers are trusted (and encouraged) to practice responsible autonomy and critical thinking, and to focus on outcomes, not conformity. At Helsing you will have a say in how we (and you!) work, the opportunity to engage on what does and doesn't work, and to take ownership of aspects of our culture that you care deeply about
What we offer
  • A focus on outcomes, not time-tracking
  • A generous compensation and benefits package (in addition to base salary) that includes, but may not be limited to, insurance coverage (medical and travel), flexible paid time off, paid holidays, and remote and/or hybrid work available depending on position. All compensation and benefits are subject to the terms and conditions of the underlying plans or programs, as applicable and as may be amended, terminated or superseded from time to time.

Helsing is an Equal Opportunity Employer. We will consider all qualified applicants without regard to race, color, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, genetics, or any other characteristic protected by applicable federal, state, or local law. Please do not submit personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data concerning your health, or data concerning your sexual orientation.
Helsing's Candidate Privacy and Confidentiality Regime can be found here.

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