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)