Research Engineer (1-year contract), SCIS

COMPANY DESCRIPTION

Singapore Management University is a place where high-level professionalism blends together with a healthy informality. The 'family-like' atmosphere among the SMU community fosters a culture where employees work, plan, organise and play together - building a strong collegiality and morale within the university.

Our commitment to attract and retain talent is ongoing. We offer attractive benefits and welfare, competitive compensation packages, and generous professional development opportunities - all to meet the work-life needs of our staff. No wonder, then, that SMU continues to be given numerous awards and recognition for its human resource excellence.

RESPONSIBILITIES

• This position is for School of Computing and Information Systems (SCIS).
• Build and maintain end-to-end data pipelines, including data ingestion, cleaning, validation, feature engineering, and dataset versioning.
• Develop and manage research codebases for optimization and machine learning experiments, ensuring reproducibility and scalability.
• Implement optimization algorithms (e.g., LP/MIP, heuristics, decomposition methods, simulation-based approaches) and integrate them with learning components.
• Develop internal tools (e.g., APIs, evaluation frameworks, benchmarking pipelines, dashboards) to support research workflows.
• Support large-scale experimentation, including performance optimization, parallelization, and reproducibility.
• Collaborate closely with researchers to translate conceptual models into robust, reusable implementations
• Other duties as assigned.

• Bachelor's or Master's degree in Computer Science, Software Engineering, Industrial Engineering, Data Science, Operations Research, or a related field.
• Strong programming skills in Python; experience with production-quality code (testing, modular design, version control).
• Experience building data and/or machine learning pipelines (e.g., pandas, SQL, workflow tools).
• Experience implementing algorithms and running large-scale experiments.
• Familiarity with Linux environments, Git, and reproducible workflows (e.g., conda, Docker).
• Proficiency in using AI tools to support development and experimentation.
• Strong problem-solving, communication, and collaboration skills.
• Preference for candidate who is familiar with
  • Optimization tools (e.g., Gurobi, CPLEX, OR-Tools, Pyomo, cvxpy, JuMP)
  • Experience with ML frameworks (e.g., PyTorch, TensorFlow, JAX) and experiment tracking tools (e.g., MLflow, Weights & Biases)
  • Experience with cloud or HPC environments and parallel computing frameworks (e.g., Ray, Dask, MPI)
  • Experience developing internal research tools (e.g., benchmarking systems, evaluation pipelines, dashboards).

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