A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
Junior AI/ML Engineer Stellantis - 3.9 Auburn Hills, MI Job Details 14 hours ago Qualifications Software engineering AI platforms (beyond public GPTs) Computational framework Machine intelligence Machine learning libraries Machine learning frameworks
Python Full Job Description Role Summary:
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.
AI & ML Development:
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
Software & Systems Engineering:
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
Delivery & Execution:
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
Growth Expectations:
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
Basic Qualifications:
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
Preferred Qualifications:
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)
