Junior AI/ML Engineer

The Junior AI/ML Engineer contributes to the design, build, and delivery of end-to-end AI/ML solutions under the guidance of senior engineers. This role is engineering-first, applying data science and machine learning as tools within well-engineered software systems.

Engineers at this level focus on well-defined implementation tasks within a larger solution, learning the full lifecycle - design, development, validation, and production handoff - through pairing, code review, and structured mentoring.

• 
  

  Implement ML models and components against established designs, using structured, time-series, and unstructured data

  
  
• 
  

  Run and document model validation, evaluation, and error analysis under senior guidance

  
  
• 
  

  Build familiarity with the team's AI/ML techniques and how they are applied to engineering, quality, and product use cases

  
  

• 
  

  Contribute production-quality code to AI systems, including:

  
  
• 
  

  Data pipelines and feature engineering

  
  
• 
  

  Model training and inference services

  
  
• 
  

  Components of agentic solutions combining LLM and other systems

  
  
• 
  

  Write clean, maintainable, and testable code (primarily Python), responding constructively to code review

  
  
• 
  

  Use the team's shared AI/ML components and engineering frameworks

  
  

• 
  

  Deliver well-scoped implementation tasks reliably, escalating blockers early

  
  
• 
  

  Participate in requirement clarification and solution iteration with the team

  
  
• 
  

  Support preparation of solutions for operationalization in partnership with MLOps teams

  
  

• 
  

  Progress toward independent ownership of implementation tasks end-to-end

  
  
• 
  

  Develop breadth across data, modeling, and software concerns

  
  
• 
  

  Actively seek and apply feedback from senior engineers

  
  

• Bachelor's degree in engineering, computer science, applied mathematics, or a related field
• A minimum of 1 year of experience
• 
  

  Solid software engineering fundamentals

  
  
• 
  

  Exposure to machine learning through coursework, internships, or projects

  
  
• 
  

  Proficiency in Python; familiarity with common ML libraries

  
  
• 
  

  Willingness to work across data, modeling, and software concerns

  
  

• 
  

  Internship or project experience deploying ML in real systems

  
  
• 
  

  Exposure to cloud-based data or ML platforms

  
  
• 
  

  Interest in LLM-based and agentic solutions

  
  
• 
  

  Familiarity with software delivery practices (version control, CI, testing)

  
  

The Junior AI/ML Engineer contributes to the design, build, and delivery of end-to-end AI/ML solutions under the guidance of senior engineers. This role is engineering-first, applying data science and machine learning as tools within well-engineered software systems.

Engineers at this level focus on well-defined implementation tasks within a larger solution, learning the full lifecycle - design, development, validation, and production handoff - through pairing, code review, and structured mentoring.

• 
  

  Implement ML models and components against established designs, using structured, time-series, and unstructured data

  
  
• 
  

  Run and document model validation, evaluation, and error analysis under senior guidance

  
  
• 
  

  Build familiarity with the team's AI/ML techniques and how they are applied to engineering, quality, and product use cases

  
  

• 
  

  Contribute production-quality code to AI systems, including:

  
  
• 
  

  Data pipelines and feature engineering

  
  
• 
  

  Model training and inference services

  
  
• 
  

  Components of agentic solutions combining LLM and other systems

  
  
• 
  

  Write clean, maintainable, and testable code (primarily Python), responding constructively to code review

  
  
• 
  

  Use the team's shared AI/ML components and engineering frameworks

  
  

• 
  

  Deliver well-scoped implementation tasks reliably, escalating blockers early

  
  
• 
  

  Participate in requirement clarification and solution iteration with the team

  
  
• 
  

  Support preparation of solutions for operationalization in partnership with MLOps teams

  
  

• 
  

  Progress toward independent ownership of implementation tasks end-to-end

  
  
• 
  

  Develop breadth across data, modeling, and software concerns

  
  
• 
  

  Actively seek and apply feedback from senior engineers