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Singapore Management University Hiring! Full Time Research Engineer (1-year contract), SCIS in - Ricebowl

Research Engineer (1-year contract), SCIS

Singapore Management University

Undisclosed

Full Time

Singapore

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Working Location

  • Singapore

Job Description

Responsibilities

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.

Qualifications

  • 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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