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Entry Level Ai Computer Science Jobs in Minnesota

We combine deep AI research expertise with the scale and operational excellence of Splunk and Cisco ... Master Degree in Computer Science, or related quantitative field, plus 2+ years of industry ...

New

We combine deep AI research expertise with the scale and operational excellence of Splunk and Cisco ... Master Degree in Computer Science, or related quantitative field, plus 2+ years of industry ...

New

Candidate has expertise in AI, machine learning, deep learning, statistical data processing ... A Bachelor's degree in a relevant field such as engineering, mathematics, computer science, health ...

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Candidate has expertise in AI, machine learning, deep learning, statistical data processing ... A Bachelor's degree in a relevant field such as engineering, mathematics, computer science, health ...

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AI Engineer

Saint Paul, MN · On-site

$110K - $130K/yr

... computer engineering, computer science, biomedical engineering, or a related field and enjoys ... AI knowledge lookup tools is a plus. We are a leading provider of remote cardiac monitoring ...

Sr. Data Scientist

Minneapolis, MN · On-site

$110K - $184K/yr

Design & implementation of AI solutions for semi-structured data, covering problem framing, data ... Bachelor's degree in data science, Computer Science, Engineering, Math, Statistics or other related ...

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Sr. Data Scientist

Minneapolis, MN · On-site

$110K - $184K/yr

Design & implementation of AI solutions for semi-structured data, covering problem framing, data ... Bachelor's degree in data science, Computer Science, Engineering, Math, Statistics or other related ...

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AI/ML Intern - Radiation Oncology

Rochester, MN

$15.25 - $19.75/hr

Interns will utilize statistics and computer science to solve complex problems in healthcare. ... AI experience/exposure or familiarity with AI principles is required. Courses and comfort level ...

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Entry Level Ai Computer Science information

What are entry level AI computer science jobs?

Entry level AI computer science jobs are positions designed for recent graduates or individuals with limited professional experience in artificial intelligence and computer science. These roles typically involve tasks such as data preprocessing, model training, testing algorithms, software development, and supporting senior engineers or researchers. Common job titles include AI Engineer, Machine Learning Engineer, Data Scientist, and AI Research Assistant. Entry level positions often require familiarity with programming languages like Python, basic knowledge of machine learning concepts, and experience working with data. These jobs provide an opportunity to build foundational skills and gain exposure to real-world AI applications.

What is the difference between Entry Level Ai Computer Science vs Data Analyst?

AspectEntry Level Ai Computer ScienceData Analyst
Required CredentialsBachelor's in CS, AI, or related field; basic programming skillsBachelor's in Statistics, Math, or related field; data analysis skills
Work EnvironmentTech companies, research labs, startups; focus on AI models and algorithmsBusiness, finance, healthcare; focus on interpreting data and generating reports
Employer & Industry UsageTech firms, AI startups, research institutionsCorporations, consulting firms, government agencies
Common Search & ComparisonEntry Level Ai Computer Science vs Data Analyst

Entry Level Ai Computer Science roles focus on developing AI models and algorithms, requiring programming and machine learning knowledge. Data Analysts interpret data to inform business decisions, emphasizing statistical analysis and reporting. While both roles work with data, AI roles are more technical and development-oriented, whereas Data Analysts focus on data interpretation and visualization.

Is AI taking entry-level computer science jobs?

Entry-level AI and computer science roles are growing as demand for AI skills, programming, and data analysis increases. However, these jobs typically require foundational knowledge in programming languages like Python, machine learning frameworks, and problem-solving skills, making them accessible to recent graduates with relevant training. Competition exists, but acquiring practical experience and certifications can improve job prospects.

What are the key skills and qualifications needed to thrive as an Entry Level AI Computer Scientist, and why are they important?

To thrive as an Entry Level AI Computer Scientist, you need a solid understanding of programming (especially Python), algorithms, and foundational knowledge in machine learning, typically supported by a degree in computer science or a related field. Familiarity with frameworks such as TensorFlow or PyTorch, experience with data analysis tools, and knowledge of version control systems like Git are commonly expected. Strong problem-solving skills, a collaborative mindset, and eagerness to learn make a candidate stand out in this rapidly evolving field. These skills are crucial for effectively building, testing, and deploying AI models while adapting to emerging technologies and team-driven projects.

What are good entry-level AI jobs?

Entry-level AI jobs include roles such as AI analyst, machine learning technician, data analyst, and research assistant, often requiring knowledge of programming languages like Python and familiarity with machine learning frameworks. These positions typically involve supporting AI development, data preprocessing, and model testing, and may require a bachelor's degree in computer science or related fields. Internships and apprenticeships in AI are also valuable starting points for gaining practical experience.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as senior AI researcher, machine learning director, or AI executive, often requiring advanced skills, extensive experience, and sometimes ownership of significant projects. These roles usually involve leadership, strategic planning, and expertise in tools like deep learning frameworks and data analysis. Compensation at this level reflects the value of specialized knowledge and impact on business outcomes.

Can I get an AI job with no experience?

Entry-level AI jobs often require some foundational knowledge of programming, machine learning concepts, and relevant tools like Python or TensorFlow. While prior experience is beneficial, candidates can improve their chances by completing online courses, certifications, or personal projects to demonstrate their skills. Employers may also consider internships or apprenticeships for those new to the field.

What are some typical projects or tasks an entry-level AI computer science professional might work on during their first year?

In an entry-level AI computer science role, you are likely to assist with tasks such as data preprocessing, implementing basic machine learning algorithms, and supporting model evaluation efforts. You may also contribute to developing or maintaining codebases for AI applications, preparing datasets, or running experiments under the guidance of senior team members. Collaboration is common—you’ll often work with data scientists, software engineers, and product managers to support larger projects and gain exposure to the full AI development lifecycle. These experiences provide a solid foundation for advancing to more complex responsibilities over time.
What are the most commonly searched types of Ai Computer Science jobs in Minnesota? The most popular types of Ai Computer Science jobs in Minnesota are:
What are popular job titles related to Entry Level Ai Computer Science jobs in Minnesota? For Entry Level Ai Computer Science jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Entry Level Ai Computer Science jobs in Minnesota look for? The top searched job categories for Entry Level Ai Computer Science jobs in Minnesota are:
What cities in Minnesota are hiring for Entry Level Ai Computer Science jobs? Cities in Minnesota with the most Entry Level Ai Computer Science job openings:
Computational Staff Scientist

$70K - $80K/yr

Other

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


Job description

Company Description:

The Center for Drug Design (CDD) at the University of Minnesota is a center of excellence in drug design and research within the College of Pharmacy. Founded in 2002 by renowned medicinal chemist Robert Vince, PhD, the CDD builds on a legacy that includes patents in anti-cancer, anti-viral, and anti-microbial therapeutics, notably the development of carbovir, marketed as Ziagen® for HIV/AIDS treatment. The Center’s mission is to unite rigorous academic research with the design and development of highly effective drugs. As part of a leading public research university, CDD offers an environment that blends academic tradition with innovation, independence, and collaboration. Team members are encouraged to pursue impactful research that advances therapeutic discovery and benefits the broader scientific and healthcare communities.


Summary of Position: 

This Staff Scientist will be under the direct supervision of the co-Director of the Center for Drug Design (CDD), with the overall goal of advancing key CDD projects in drug discovery, by providing center-wide computational chemistry / biology support. The main duties will be to conduct molecular docking to aid in structure-based drug design, carry out virtual screenings using structure- or ligand-based approaches, free energy calculations, and predict protein structures as well as drug ADME and toxicity properties. The success of this position will require expertise in quantum chemistry, molecular mechanics methods, and molecular dynamic simulation, skills in molecular modeling and protein structure prediction, the ability to work in a highly collaborative team environment, and working knowledge in AI / ML in drug discovery. The successful candidate is expected to work largely independently with minimal supervision. 


Principal Duties and Responsibilities:

Cheminformatics and molecular modeling (85%)

  • Conduct molecular docking to aid in structure-based drug design.
  • Carry out virtual screenings using structure- or ligand-based approaches.
  • Perform free energy calculations.
  • Predict protein structures.
  • Curate and maintain a searchable in-house compound database.
  • Create compound structure data files suitable for high throughput docking.
  • Predict drug ADME and toxicity properties in silico using computational and AI models.

Other duties (15%)

  • Equipment management
  • Molecular docking user training


Required Qualifications:

  • Ph.D. in computational chemistry, Cheminformatics, Bioinformatics, Biophysics or a related field.
  • Strong foundation in theoretical physical chemistry and organic chemistry.
  • Demonstrated expertise in quantum chemistry, molecular mechanics methods, and molecular dynamic simulation.
  • Demonstrated knowledge on protein-ligand molecular interactions.
  • Proven record in productive scientific research.
  • Ability to work effectively both independently and collaboratively.
  • Strong interpersonal and communication skills.


Preferred Qualifications:

  • Demonstrated experience in computer-aided drug design, including molecular docking, AlphaFold structural prediction, structure- or ligand-based virtual screening, free energy calculations.
  • Demonstrated user experience with one or more of the standard modeling program suites (Schrödinger, MOE, etc).
  • Proven ability to write project reports and manuscripts.
  • Ability to work independently, design and execute research projects, and communicate scientific findings, with minimal supervision.


Pay and Benefits:

  • Full time - 100% Appointment
  • Pay range: $70,000 - $80,000 depending on education, qualifications and experience
  • The position is 100% on site.
  • Please visit the Office of Human Resources website for more information regarding benefit eligibility.


How to Apply:

Submit the following as a single .pdf file to cdd@umn.edu:

  • CV
  • Cover letter
  • Name and contact information for three professional references familiar with your scientific experience (e.g., PhD advisor, postdoctoral advisor, mentors, or research supervisors).