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Machine Learning Engineer From Home Jobs in Virginia

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Machine Learning Engineer

Reston, VA · On-site

Machine Learning Engineer Location: Fort Meade, MD Required Clearance : TS/SCI w/ Full-Scope Poly Salary: Competitive We are seeking a highly skilled and motivated Machine Learning Engineer to join ...

Stellent IT LLC

Machine Learning Engineer

Arlington, VA · On-site

$130K/yr

Machine Learning Engineer Location: Arlington, VA Duration: FTE/No C2C Role: Design, develop, and maintain the product ionization of machine learning, deep learning, generative AI, large language ...

Cymertek

Machine Learning Engineer

Chantilly, VA · On-site

Machine Learning Engineer LOCATION Chantilly, VA 20151 CLEARANCE TS/SCI Full Poly (Please note this ... From employee and family events to career-long support, we create a community you'll never want to ...

Cymertek

Machine Learning Engineer

Tysons, VA · On-site

Machine Learning Engineer LOCATION Tysons, VA 22182 CLEARANCE TS/SCI Full Poly (Please note this ... From employee and family events to career-long support, we create a community you'll never want to ...

Cymertek

Machine Learning Engineer

Reston, VA · On-site

Machine Learning Engineer LOCATION Reston, VA 20190 CLEARANCE TS/SCI Full Poly (Please note this ... From employee and family events to career-long support, we create a community you'll never want to ...

Cymertek

Machine Learning Engineer

Reston, VA · On-site

Machine Learning Engineer LOCATIONReston, VA 20190 CLEARANCETS/SCI Full Poly (Please note this ... From employee and family events to career-long support, we create a community you'll never want to ...

Booz Allen Hamilton

Machine Learning Engineer

Mclean, VA · On-site

$77K - $176K/yr

As a machine learning engineer on our defense technology team, you'll train, test, deploy, and maintain models that learn from data. In this role, you'll own and define the direction of mission ...

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Machine Learning Engineer From Home information

What is the difference between Machine Learning Engineer From Home vs Data Scientist?

AspectMachine Learning Engineer From HomeData Scientist
Required CredentialsBachelor's/Master's in CS, ML, or related; experience with ML frameworksBachelor's/Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentRemote, flexible hours, often project-basedRemote or on-site, collaborative teams, research-focused
Employer & Industry UsageTech companies, startups, AI firmsTech, finance, healthcare, research institutions
Common Search & ComparisonOften compared for technical skills and remote work optionsCompared for data analysis and modeling expertise

While both roles require strong technical credentials and often involve remote work, Machine Learning Engineers From Home focus on developing and deploying ML models, whereas Data Scientists analyze data to generate insights. The choice depends on whether you prefer building algorithms or interpreting data trends.

What are the most commonly searched types of Machine Learning Engineer jobs in Virginia? The most popular types of Machine Learning Engineer jobs in Virginia are:
What are popular job titles related to Machine Learning Engineer From Home jobs in Virginia? For Machine Learning Engineer From Home jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer From Home jobs in Virginia look for? The top searched job categories for Machine Learning Engineer From Home jobs in Virginia are:
What cities in Virginia are hiring for Machine Learning Engineer From Home jobs? Cities in Virginia with the most Machine Learning Engineer From Home job openings:
Machine Learning Engineer From Home jobs near you

Machine Learning Engineer

Full Scope

Reston, VA • On-site

Other

Re-posted 22 days ago

Job description

Job Title:Machine Learning Engineer
Location:Fort Meade, MD
Required Clearance: TS/SCI w/ Full-Scope Poly
Salary:Competitive
We are seeking a highly skilled and motivated Machine Learning Engineer to join our dynamic team. The ideal candidate will have a strong background in machine learning, data science, and software engineering. You will work closely with data scientists, engineers, and product managers to design, develop, and deploy machine learning models and solutions that drive business value.
Key Responsibilities:
  • Design, develop, and implement machine learning models and algorithms to solve real-world problems.
  • Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
  • Conduct data analysis and preprocessing to ensure high-quality data for model training.
  • Optimize and fine-tune models for performance, accuracy, and scalability.
  • Deploy machine learning models into production and monitor their performance.
  • Develop and maintain machine learning pipelines and infrastructure.
  • Stay current with the latest research and advancements in machine learning and AI.
  • Participate in code reviews, team meetings, and contribute to a collaborative development environment.
  • Document processes, models, and findings comprehensively.
Qualifications:
  • Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field. Ph.D. is a plus.
  • Proven experience as a Machine Learning Engineer or in a similar role.
  • Strong proficiency in programming languages such as Python, R, or Java.
  • Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, etc.
  • Solid understanding of machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and deep learning.
  • Experience with data processing tools like Pandas, NumPy, and data visualization tools such as Matplotlib or Seaborn.
  • Familiarity with cloud platforms like AWS, Google Cloud, or Azure for model deployment and scaling.
  • Strong problem-solving skills and the ability to think critically and analytically.
  • Excellent communication and teamwork skills.
Preferred Qualifications:
  • Experience with natural language processing (NLP) and computer vision.
  • Familiarity with big data technologies such as Hadoop, Spark, or Kafka.
  • Knowledge of software development best practices and version control systems like Git.
  • Experience with containerization tools like Docker and orchestration tools like Kubernetes.
  • Previous experience in a fast-paced, startup environment.

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