Machine Learning Engineer

Job Title: Machine Learning Engineer

Start Date: January 2026 (first week)

Education Requirement: PhD or Postdoctoral experience strongly preferred. Candidates graduating in December 2025 are welcome to apply.

Industrial & Operations Engineering (IOE) / Data Science

About the Role

We are seeking a highly skilled Machine Learning Engineer with a strong background in predictive modeling, data science, statistics, and industrial & operational engineering (IOE). The ideal candidate will be creative in building innovative, data-driven models and capable of applying advanced analytical techniques to solve complex, real-world problems.

Key Responsibilities

• Develop, implement, and optimize machine learning models for predictive analytics and decision support.
• Apply statistical methods and data-science techniques to large, complex datasets.
• Build scalable data-driven modeling frameworks and analytical tools.
• Collaborate with cross-functional teams to translate engineering and operational challenges into ML-based solutions.
• Communicate technical results clearly to both technical and non-technical audiences.
• Stay current with emerging research and trends in ML, statistics, and IOE.

Required Qualifications

• PhD in Machine Learning, Data Science, Statistics, Computer Science, Industrial & Operations Engineering, or a related field.
• Postdoctoral experience preferred, but not required.
• Candidates completing their PhD by December 2025 will be considered.
• Strong knowledge of predictive modeling, statistical analysis, and data science methodologies.
• Demonstrated experience with creative modeling approaches for data-driven solutions.
• Proficiency in ML tools and languages (Python, R, TensorFlow, PyTorch, etc.).
• Strong analytical, problem-solving, and communication skills.

Preferred Skills (Nice to Have)

• Experience with industrial systems, optimization, or operations research.
• Publications in relevant ML, data science, or engineering conferences/journals.
• Experience working with real-world operational datasets.

Interview Process

Total Duration: ~1 hour

Presentation (20-30 minutes): Candidate will present an academic project, dissertation work, or relevant work from previous employment.

Q&A Session: Open discussion with the interview panel following the presentation.