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No Experience Machine Learning Data Annotation Jobs in Virginia

USM

JB061715 - Data Scientist

Mclean, VA · On-site

... Develop machine learning algorithm Identify trends and business insights from data Create ... dashboards and visualization Work with stakeholders to solve business problem Present findings and ...

Booz Allen Hamilton

Machine Learning Engineer

Mclean, VA · On-site

$77K - $176K/yr

Machine Learning Engineer The Opportunity: As an experienced AI and ML engineer, you know that ... Experience with data handling and model development * Ability to solve client issues and problems

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No Experience Machine Learning Data Annotation information

Can I get an AI job with no experience?

Entry-level machine learning data annotation roles often do not require prior experience, as they focus on labeling data to train AI models. Basic computer skills, attention to detail, and understanding of data formats are typically sufficient to start, and some companies offer training or onboarding programs for beginners.

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.

Is it hard to get hired for data annotation?

Getting hired for a data annotation role with no experience is generally accessible, as many companies offer entry-level positions that require basic computer skills and attention to detail. Candidates often complete simple training or tutorials, and the job typically involves straightforward tasks using annotation tools. While competition exists, building a strong attention to detail and familiarity with annotation platforms can improve chances of employment.

Do I need coding experience for data annotation?

For a no experience machine learning data annotation role, coding experience is generally not required, as the job involves labeling data such as images, text, or audio using user-friendly tools. Basic computer skills and attention to detail are more important, though some tasks may benefit from familiarity with simple software or platforms. Coding skills are typically not necessary for entry-level annotation positions.

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.

Can you work for data annotation with no experience?

No experience data annotation roles are often entry-level and may not require prior skills, but basic computer literacy and attention to detail are usually necessary. Training is typically provided, and familiarity with annotation tools can be helpful for efficiency. Having some understanding of data labeling standards can improve job performance and opportunities for advancement.
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No Experience Machine Learning Data Annotation jobs near you
Senior Data Scientist / AI Machine Learning Research Engineer

Senior Data Scientist / AI Machine Learning Research Engineer

CACI International, Inc.

Sterling, VA • On-site

$113K - $237K/yr

Full-time

Medical, Retirement, PTO

Posted 26 days ago

Job description

Job Title: Senior Data Scientist / AI Machine Learning Research Engineer
Job Category: Science
Time Type: Full time
Minimum Clearance Required to Start: TS/SCI
Employee Type: Regular
Percentage of Travel Required: Up to 10%
Type of Travel: Continental US
* * *
The Opportunity:
CACI has an exciting new opportunity for a Senior AI and Machine Learning Research Engineer. Apply machine learning, statistics, to develop algorithms to solve challenging problems, signal processing, and computer networking domains. In this role, you'll leverage your strong foundation in machine learning, data science, and signal processing to solve complex challenges in the RF domain.
Responsibilities:
1. Strong mathematical foundation in statistics, linear algebra, and calculus with demonstrated ability to understand and implement machine learning algorithms from first principles rather than solely relying on pre-built libraries.
2. Proficiency in designing and building data pipelines, including experience with ETL processes, data warehousing solutions, and optimizing workflows for large-scale data processing.
3. Hands-on experience with cloud-based infrastructure (e.g., AWS, Azure, GCP) for deploying ML solutions, including containerization, orchestration, and CI/CD pipelines for model deployment.
4. Programming expertise in Python and SQL, with experience using data engineering frameworks (e.g., Spark, Airflow) and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
5. Demonstrated experience in establishing ML governance practices, including version control for datasets and models, experiment tracking, model monitoring, and implementing reproducible research principles.
Qualifications:
Required:
  • Master's degree in quantitative field with mathematical underpinnings and at least 7 years' experience.
  • Experience developing models,.
  • Strong background in machine learning, mathematics and statistics.
  • Comfortable using Linux operating systems and commonly used Linux utilities.
  • Must be a US Citizen with the ability to obtain, maintain and/or transfer the required security clearance as dictated by the contract
  • Must have active Top Secret Clearance with the ability to obtain SCI with Polygraph

Desired:
  • Ph.D. in computer science, computer engineering, or machine learning, Statistics, applied mathematics or Physics.
  • Experience applying machine learning to signal processing and/or other time-series data analysis applications.
  • Knowledge of or experience with information theory, probability theory, parametric and non-parametric statistical tests.
  • Familiarity with concepts and techniques associated with adversarial AI and AI/ML assurance.
  • Active Top Secret/SCI clearance preferred.

What You Can Expect:
A culture of integrity.
At CACI, we place character and innovation at the center of everything we do. As a valued team member, you'll be part of a high-performing group dedicated to our customer's missions and driven by a higher purpose - to ensure the safety of our nation.
An environment of trust.
CACI values the unique contributions that every employee brings to our company and our customers - every day. You'll have the autonomy to take the time you need through a unique flexible time off benefit and have access to robust learning resources to make your ambitions a reality.
A focus on continuous growth.
Together, we will advance our nation's most critical missions, build on our lengthy track record of business success, and find opportunities to break new ground - in your career and in our legacy.
Pay Range:
There are a host of factors that can influence final salary including, but not limited to, geographic location, Federal Government contract labor categories and contract wage rates, relevant prior work experience, specific skills and competencies, education, and certifications. Our employees value the flexibility at CACI that allows them to balance quality work and their personal lives. We offer competitive compensation, benefits and learning and development opportunities. Our broad and competitive mix of benefits options is designed to support and protect employees and their families. At CACI, you will receive comprehensive benefits such as; healthcare, wellness, financial, retirement, family support, continuing education, and time off benefits.
The proposed salary range for this position is:
$113,200 - $237,800
CACI is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, age, national origin, disability, status as a protected veteran, or any other protected characteristic.

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