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Machine Learning Engineer From Home Jobs in Virginia

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 ...

GCP/AWS Machine Learning Engineer Freddie Mac iLab is currently looking for Machine Learning Engineers in its Innovation Labs - Tech Strategy team. In this position, you will be responsible for ...

The Machine Learning Engineer will leverage their strong technical background and knowledge to ... models using data from across the enterprise to support mission and business functions.

Machine Learning Engineer Our client, a financial company, is looking for a Machine Learning Engineer for their McLean, VA location. Requirements: * Python, AWS, Kubernetes, Kubeflow, MLOps, ML ...

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 ...

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 ...

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 ...

Machine Learning Engineer Mount Indie is looking for a Machine Learning (ML) Engineer with ... Ability to understand and implement research-level algorithms from technical papers and reports

Machine Learning Engineer D.C. Area About the Position As a member of our Engineering team, you ... from data science peers to business decision-makers * Demonstrated exploratory analysis and ...

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Machine Learning Engineer From Home information

What jobs make $3,000 a month without a degree?

Machine Learning Engineers typically require a degree, but some related roles like data annotators, technical support specialists, or freelance AI content creators can earn around $3,000 monthly without a formal degree, often relying on skills, certifications, or experience. These jobs may involve working remotely and utilizing online tools or platforms to find opportunities.

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 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

Full Scope

Reston, VA

Other

Posted 12 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.