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Entry Level Computer Science Artificial Intelligence Jobs in Washington, DC

Varsity Tutors

College Computer Science Tutor

Laurel, MD · Remote

$18 - $40/hr

Emphasizes theoretical foundations alongside practical implementation and connects computer science to artificial intelligence, distributed systems, and industry engineering practices. * Curriculum ...

Varsity Tutors

College Computer Science Tutor

Fairfax, VA · Remote

$18 - $40/hr

Emphasizes theoretical foundations alongside practical implementation and connects computer science to artificial intelligence, distributed systems, and industry engineering practices. * Curriculum ...

Varsity Tutors

College Computer Science Tutor

Bowie, MD · Remote

$18 - $40/hr

Emphasizes theoretical foundations alongside practical implementation and connects computer science to artificial intelligence, distributed systems, and industry engineering practices. * Curriculum ...

Howard University

Assistant Professor

Washington, DC · On-site

D. in Mathematics, Applied Mathematics, Computer Science, Data Science, Artificial Intelligence, or a closely related field. • Strong research record in mathematics-driven AI or ML, with ...

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All JobsComputer Science Artificial Intelligence JobsEntry Level Computer Science Artificial Intelligence JobsEntry Level Computer Science Artificial Intelligence Jobs in Washington, DC

Entry Level Computer Science Artificial Intelligence information

See Washington, DC salary details

$15

$19

$23

How much do entry level computer science artificial intelligence jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for entry level computer science artificial intelligence in Washington, DC is $19.17, according to ZipRecruiter salary data. Most workers in this role earn between $17.69 and $19.62 per hour, depending on experience, location, and employer.

Is artificial intelligence taking entry-level computer science jobs?

Entry-level computer science jobs involving artificial intelligence are growing as companies seek new talent with skills in machine learning, programming, and data analysis. However, competition remains high, and candidates with relevant internships, certifications, or knowledge of AI tools like Python and TensorFlow have better prospects. Staying current with industry trends and developing practical skills are important for entry-level applicants in this field.

What is the difference between Entry Level Computer Science Artificial Intelligence vs Entry Level Data Science?

AspectEntry Level Computer Science Artificial IntelligenceEntry Level Data Science
Required CredentialsBachelor's in Computer Science, AI, or related fields; knowledge of programming, machine learning, and algorithmsBachelor's in Data Science, Statistics, or related fields; skills in programming, statistics, and data analysis
Work EnvironmentTech companies, research labs, startups focusing on AI applicationsBusiness, finance, healthcare, and tech firms analyzing data for insights
Employer & Industry UsageUsed in AI development, machine learning projects, and automationUsed in data analysis, predictive modeling, and business intelligence

Both roles require programming skills and a strong foundation in computer science. While AI focuses on developing intelligent systems and algorithms, data science emphasizes analyzing data to inform decisions. The choice depends on your interest in creating AI solutions versus extracting insights from data.

What is an entry level computer science artificial intelligence job?

Entry Level Computer Science Artificial Intelligence jobs are positions designed for recent graduates or individuals with limited professional experience in computer science, specifically focusing on artificial intelligence (AI). These roles typically involve assisting in the development, testing, and deployment of AI models and algorithms under the guidance of more experienced engineers or data scientists. Tasks may include data preprocessing, writing code for machine learning models, evaluating model performance, and collaborating with teams to solve real-world problems using AI. Entry-level AI jobs provide an opportunity to gain practical experience, learn industry-standard tools and frameworks, and build foundational skills for a career in artificial intelligence.

What types of projects can I expect to work on as an entry level computer science artificial intelligence professional?

As an entry-level AI professional, you’ll typically contribute to projects involving data preprocessing, model training, and algorithm implementation under the guidance of senior team members. You might work on tasks like cleaning datasets, developing and testing machine learning models, and assisting in deploying AI solutions. Collaboration with software engineers, data scientists, and product managers is common, providing you with a well-rounded introduction to real-world AI development and teamwork. These projects help you build foundational skills while gaining exposure to various AI applications and industry tools.

What are the key skills and qualifications needed to thrive as an entry level computer science artificial intelligence professional?

To excel in an entry-level AI role, you need a solid understanding of computer science fundamentals, programming languages like Python, and basic knowledge of algorithms and data structures, typically supported by a relevant degree. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), data analysis tools, and version control systems is highly valued. Strong problem-solving skills, curiosity, and effective teamwork set candidates apart in this field. These abilities are crucial for building robust AI solutions, adapting to technological advances, and collaborating efficiently on complex projects.
What are the most commonly searched types of Computer Science Artificial Intelligence jobs in Washington, DC? The most popular types of Computer Science Artificial Intelligence jobs in Washington, DC are:
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Entry Level Computer Science Artificial Intelligence jobs near you
Infographic showing various Entry Level Computer Science Artificial Intelligence job openings in Washington, DC as of July 2026, with employment types broken down into 82% Full Time, 15% Part Time, and 3% Contract. Highlights an 88% Physical, 5% Hybrid, and 7% Remote job distribution, with an average salary of $39,867 per year, or $19.2 per hour.

Artificial Intelligence Cybersecurity Engineer

Entarian

Arlington, VA • On-site

Full-time

Re-posted 10 days ago

Job description

We are seeking a skilled Artificial Intelligence Cybersecurity Engineer to join our team and ensure the seamless deployment, monitoring, and optimization of AI models in production. 

Sev1Tech is seeking an AI Integration Engineer to integrate AI models into production systems, ensuring robust performance, real-time monitoring, and secure operations. The AI Integration Engineer will bridge the gap between AI model development and production systems, integrating models into applications, APIs, and infrastructure. This role focuses on building dashboards for real-time and historical model health, detecting data drift, and managing AI logging, while ensuring secure-by-design practices and alignment with business objectives. 

Key Responsibilities 

  • Model Integration: Integrate AI/ML models into applications (e.g., web, mobile, IoT) using APIs (REST, gRPC) and platforms like TensorFlow Serving or AWS SageMaker. 
  • Dashboard Development: Create real-time and historical dashboards using Grafana, Kibana, or Plotly to monitor model health (e.g., latency, accuracy) and data drift. 
  • Drift and Health Monitoring: Implement monitoring pipelines with tools like Evidently AI or Weights & Biases to detect data drift and model degradation, triggering alerts as needed. 
  • Logging and Tracing: Set up logging systems with ELK Stack, OpenTelemetry, or LangSmith to capture AI events, errors, and traces for debugging and auditing. 
  • Security Implementation: Apply secure-by-design principles to protect models and data from vulnerabilities (e.g., adversarial attacks, data leakage) using tools like Adversarial Robustness Toolbox (ART). 
  • System Optimization: Optimize model inference for performance (e.g., via quantization, edge deployment) and ensure compatibility with cloud (AWS, Azure) or on-premises infrastructure. 
  • Collaboration: Partner with data scientists to understand model requirements, DevOps for infrastructure alignment, and stakeholders for reporting needs. 
  • Testing and Validation: Perform end-to-end testing of AI integrations, including stress testing and validation of dashboard metrics. 
  • Compliance: Ensure integrations comply with regulations like GDPR, HIPAA, or NIST AI RMF for secure data handling. 

    • Education: Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a related field. 
    • Experience: 
    • 4+ years in software engineering or AI integration, with experience deploying AI models in production. 
    • Hands-on experience with dashboarding tools (e.g., Grafana, Kibana) and observability platforms (e.g., Prometheus, Datadog). 
    • Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) for AI deployment. 
    • Technical Skills: 
    • Proficiency in Python; knowledge of JavaScript, C++, or Go is a plus for UI or system-level integration. 
    • Experience with containerization (Docker, Kubernetes) and API development (REST, GraphQL). 
    • Expertise in logging frameworks (e.g., ELK Stack, OpenTelemetry) and visualization tools (e.g., Plotly, Chart.js). 
    • AI-Specific Skills: 
    • Understanding of AI model metrics (e.g., F1 score, latency) and drift detection techniques (e.g., PSI, KS test). 
    • Knowledge of AI vulnerabilities (e.g., prompt injection, model inversion) and mitigation strategies (e.g., differential privacy, ART). 
    • Soft Skills: 
    • Strong problem-solving skills for debugging integration issues and optimizing dashboards. 
    • Excellent communication to translate technical metrics into business insights. 
    • Collaboration skills to work across data science, DevOps, and product teams.
    • *Must be eligible to obtain a Department of Homeland Security EOD clearance (Requirements 1. US Citizenship, 2. Favorable Background Investigation) 
  •  

    • Experience with LLM-specific tools like LangSmith or Helicone for monitoring generative AI applications. 

    Apply