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What are the key skills and qualifications needed to thrive as a New Grad Machine Learning Engineer, and why are they important?

To thrive as a New Grad Machine Learning Engineer, you need a solid foundation in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch, version control systems like Git, and coursework or certification in data science are highly beneficial. Strong problem-solving abilities, curiosity, and effective communication skills help you collaborate and convey complex technical concepts to diverse teams. These skills and qualities are essential for developing innovative models, ensuring project success, and integrating seamlessly into fast-paced tech environments.

What are some typical challenges new graduates might face when starting out in a machine learning role, and how can they overcome them?

New grad machine learning engineers often encounter challenges such as bridging the gap between academic knowledge and practical, production-level projects. Adapting to real-world data issues, collaborating with cross-functional teams, and understanding scalable deployment can be daunting at first. To overcome these, it's helpful to seek mentorship, proactively ask questions, and dedicate time to learning best practices in code versioning, model evaluation, and team communication. Engaging in code reviews and participating in team discussions can also accelerate the learning curve and foster professional growth.

What are 'New Grad Machine Learning' roles?

New Grad Machine Learning roles are entry-level positions designed for recent graduates who have studied machine learning, artificial intelligence, data science, or related fields. These positions typically involve working with experienced data scientists and engineers to develop, implement, and improve machine learning models and algorithms. New grads in these roles often contribute to projects involving data preprocessing, model training, evaluation, and deployment. The goal is to help new graduates gain hands-on experience and grow their skills in a real-world setting while contributing to the organization's AI initiatives.

What is the difference between New Grad Machine Learning vs Data Scientist?

AspectNew Grad Machine LearningData Scientist
Required CredentialsBachelor's in CS, Data Science, or related field; some internshipsBachelor's or Master's in CS, Statistics, or related; some experience
Work EnvironmentEntry-level, team-focused, research and developmentData analysis, modeling, cross-functional collaboration
Employer & Industry UsageTech companies, startups, research labsTech, finance, healthcare, consulting firms

New Grad Machine Learning roles typically focus on foundational skills, internships, and entry-level tasks, while Data Scientist positions often require more experience in data analysis and statistical modeling. Both roles are common in tech industries, but Data Scientists usually handle broader data analysis responsibilities.

What are popular job titles related to New Grad Machine Learning jobs in Maryland? For New Grad Machine Learning jobs in Maryland, the most frequently searched job titles are:
What job categories do people searching New Grad Machine Learning jobs in Maryland look for? The top searched job categories for New Grad Machine Learning jobs in Maryland are:
What cities in Maryland are hiring for New Grad Machine Learning jobs? Cities in Maryland with the most New Grad Machine Learning job openings:
New Grad Machine Learning jobs near you
Machine Learning Engineer (Laurel, MD)

Machine Learning Engineer (Laurel, MD)

Shield Consulting Solutions

Laurel, MD • On-site

$220K - $230K/yr

Other

Medical, Retirement, PTO

This job post has expired today. Applications are no longer accepted.

Job description

**Active TS/SCI w/Polygraph REQUIRED** Please do not apply if you do not currently possess this level of clearance.
Telework: None
Basic Requirements:

  • 14 years of experience as a software engineer
  • Bachelor's degree in a technical discipline
    • 4 additional years of experience as a software engineer may be substituted for a degree

Job Description:
  • Implement data pipelines at scale, including both the synthesizing of new pipelines and the refactoring of existing pipelines to improve efficiency and code correctness.
  • Monitor and improve existing data science tools in support of transition from development to production systems.
  • Design, implement, and enhance ML analytics using a wide variety of Python libraries including, but limited to, PyTorch, NumPy, Pandas, and Scikit-learn.
  • Train, test, track and curate models using standard tools and practices.
  • Integrate GitOps for continuous integration and deployment of models using Docker scaling.
  • Use AWS services such as EC2, S3, and RDS for building and deploying applications.
  • Integrate model and tool outputs within Computer Network Defense (CND) systems and/or software tools to enhance, implement, and maintain the Security Management/Monitoring services and capabilities within the Agency IT Enterprise.
  • Document all processes and code; provide comprehensive reports on the completed tasks.

Required Experience:
  • Python
  • Docker and/or Kubernetes

Desired Experience:
  • AWS
  • Machine learning

Salary: $220,000 - $230,000 annually
Excellent benefits package including 25 days PTO, 11 paid holidays, 100% employer-paid healthcare for employees and dependents - available day 1, 8% 401(k) employer match - immediate vesting.
Disclaimer: The salary range provided is an estimate based on current market conditions and may be adjusted based on factors such as experience, skills, and qualifications. The final salary offer will be determined after a thorough review of the candidate's background and alignment with the role. Please note that this range is subject to change and should be considered as a guideline rather than a definitive figure.
Shield Consulting Solutions is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law.
This is a full time position

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