Machine Learning Engineer Intern at mDoc Healthcare

Key Responsibilities:

Prompt Engineering & Optimization

• Modify and optimize prompts for large language models
• Test prompt variations and document performance improvements
• Maintain prompt libraries and version control
• Test prompt performance and effectiveness

Research & Analysis

• Stay current with latest developments in LLMs and AI research
• Prepare visual reports and summaries for meetings
• Create presentations about ML work and project outcomes when needed
• Create technical documentation and research reports

System Monitoring & Maintenance

• Monitor system logs and performance metrics
• Set up and maintain logging systems
• Generate regular performance reports
• Identify and escalate system anomalies or issues

General Support

• Assist with data preparation and preprocessing tasks
• Support model evaluation and testing activities
• Participate in team meetings and knowledge sharing sessions

Required Qualifications:

Technical Skills:

• BHS degree in Computer Science, Data Science, Machine Learning, or related field
• Strong programming skills in Python
• Familiarity with machine learning concepts and frameworks
• Strong understanding of (LLMs) Large Language Models (GPT, Claude, etc.)
• Experience with prompt engineering techniques
• Knowledge of logging systems and monitoring tools
• Skilled in Google Workspace applications (Docs, Sheets, Slides) for documentation and collaboration

Research Capabilities:

• Strong analytical and research skills
• Ability to read and summarize technical papers
• Experience with literature reviews and research methodologies
• Familiarity with ML research platforms (ArXiv, Papers with Code, etc.)

Soft Skills:

• Excellent written and verbal communication
• Detail-oriented with strong organizational skills
• Ability to work independently and manage multiple tasks
• Collaborative team player
• Proactive problem-solving approach

Preferred Qualifications:

• Previous internship or project experience in GenAI/ML/AI
• Experience with cloud platforms (GCP)
• Familiarity with MLOps practices and tools
• Knowledge of version control systems (Git)
• Experience with data visualization tools