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No Experience Machine Learning Data Annotation Jobs in Virginia

Seekr

Senior Data Scientist

Reston, VA · On-site

We are a fast-growing company where no one is a bystander. We offer you the opportunity to make a ... Experience with interactive machine learning (eg. active learning, reinforcement learning, machine ...

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All JobsMachine Learning Data Annotation JobsNo Experience Machine Learning Data Annotation JobsNo Experience Machine Learning Data Annotation Jobs in Virginia

No Experience Machine Learning Data Annotation information

What should I expect when collaborating with machine learning engineers as a data annotator with no prior experience?

As a data annotator working alongside machine learning engineers, you will play a vital role in preparing high-quality labeled data for model training. Engineers often provide clear guidelines and feedback on how to label or categorize data accurately, and they may hold regular check-ins to address questions and ensure consistency. While you may not need technical expertise, strong communication and attention to detail are essential, as your work directly impacts the performance of machine learning models. Over time, you’ll become familiar with annotation tools and may have the opportunity to take on more advanced tasks or quality assurance responsibilities.

What are 'No Experience Machine Learning Data Annotation' jobs?

'No Experience Machine Learning Data Annotation' jobs are entry-level positions where individuals help label and categorize data used to train machine learning models. These roles do not require prior experience in data science or programming, making them accessible to beginners. Typical tasks may include tagging images, transcribing audio, or identifying objects in videos. These jobs are essential for improving the accuracy of AI systems and are often done remotely or on a flexible schedule.

What are the key skills and qualifications needed to thrive as a No Experience Machine Learning Data Annotation Specialist, and why are they important?

To succeed in a No Experience Machine Learning Data Annotation role, you need strong attention to detail, basic computer literacy, and the ability to follow precise instructions, often requiring at least a high school diploma. Familiarity with data labeling tools (like Labelbox or Supervisely) and experience with spreadsheet software are typically helpful, though many positions offer on-the-job training. Reliability, patience, and effective communication are valuable soft skills for maintaining quality and meeting deadlines. These skills ensure accurate, consistent data labeling, which is critical for training reliable machine learning models.

What is the difference between No Experience Machine Learning Data Annotation vs Data Labeling Specialist?

AspectNo Experience Machine Learning Data AnnotationData Labeling Specialist
Required CredentialsNo formal experience needed, training providedTypically similar, may require basic technical skills
Work EnvironmentRemote or office-based, repetitive tasksRemote or onsite, focused on data preparation
Industry UsageCommon in AI/ML companies, tech startupsUsed across tech, automotive, healthcare sectors
Search & Comparison IntentOften searched by beginners or entry-level job seekersCompared for skill requirements and job scope

Both roles involve labeling data for machine learning models, with minimal experience required. Data Labeling Specialists may have slightly more specialized tasks, but both are entry-level positions vital for AI development.

What are the most commonly searched types of Machine Learning Data Annotation jobs in Virginia? The most popular types of Machine Learning Data Annotation jobs in Virginia are:
What are popular job titles related to No Experience Machine Learning Data Annotation jobs in Virginia? For No Experience Machine Learning Data Annotation jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching No Experience Machine Learning Data Annotation jobs in Virginia look for? The top searched job categories for No Experience Machine Learning Data Annotation jobs in Virginia are:
What cities in Virginia are hiring for No Experience Machine Learning Data Annotation jobs? Cities in Virginia with the most No Experience Machine Learning Data Annotation job openings:
No Experience Machine Learning Data Annotation jobs near you
Machine Learning Engineer - Computer Vision

Machine Learning Engineer - Computer Vision

CaseGuard

Arlington, VA

Other

Medical, Dental, Vision, Retirement, PTO

Re-posted 5 days ago

Job description

We are seeking a highly skilled and motivated Machine Learning Engineer specializing in Computer Vision to join our team. The ideal candidate will have a strong background in developing and deploying machine learning models focused on image and video processing. You will work closely with cross-functional teams to design, implement, and optimize vision-based AI solutions to address real-world challenges.

Key Responsibilities:

  • Design, develop, and deploy computer vision models for tasks such as object detection, object tracking, video segmentation, and facial recognition.
  • Optimize and fine-tune deep learning algorithms for real-time performance.
  • Work closely with the software engineers and product teams to identify opportunities for leveraging data.
  • Collect, clean, and preprocess large datasets to prepare for model training and evaluation.
  • Evaluate and optimize machine learning models for accuracy, performance, and scalability.
  • Deploy models into production environments and monitor their performance to ensure reliability.
  • Stay up-to-date with the latest advancements in computer vision and artificial intelligence.
  • Collaborate with cross-functional teams to integrate machine learning solutions into business processes.
  • Document processes, models, and implementations to ensure reproducibility and scalability.

Required Qualifications:

  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
  • Experience in deep learning models, their training, and hyperparameter tuning using libraries such as TensorFlow, PyTorch, and Transformers or other Huggingface tools.
  • Experience with data manipulation tools such as Pandas, NumPy, and SQL.
  • Strong programming skills in Python and C++.
  • Experience in MLOps principles and model deployment and instrumentation on cloud platforms such as AWS, Azure, or Google Cloud for model deployment and knowledge with efficient serving tools such as ONNX, triton, and vllm.
  • Proficiency in working with image and video data, including preprocessing and augmentation techniques.
  • Strong understanding of machine learning algorithms, including supervised and unsupervised learning and deep learning.
  • Strong communication skills and the ability to work collaboratively in a team environment.

Great to have:

  • Familiarity with containerization and orchestration tools like Docker and Kubernetes.
  • Experience with version control systems such as Git.
  • Understanding software engineering best practices, including code review, testing, and documentation.
  • Experience with Large Language Models (LLMs) is a great plus.
  • Experience with data annotation tools and processes.

Benefits:

  • Competitive Salary
  • Stock Option
  • Medical, Dental, and Vision Insurance
  • 401K
  • Paid Vacation
  • Ten paid holidays per year
  • Friendly and Learning environment
About CaseGuard

CaseGuard is a software company that helps law enforcement agencies, federal agencies, hospitals, schools, airports, and others manage all their media redaction needs in one easy-to-use redaction software. CaseGuard Studio is one of a kind. Our team is driven by a passion for great software design, creating great products, and creative processes; CaseGuard implements innovative ideas across multiple services and agencies. We invest in people. We nurture skills consistent with our values and our future strategy. Our passionate pursuit of excellence, the application of our creativity to solve our clients' challenges, our technical expertise, and our collaborative spirit are measures of our success.

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