Research Engineer (1-year contract), SCIS

Singapore Management University; Singapore; On-site; SGD 70,000 - 110,000; Full time

Job summary

Singapore Management University (SMU) in Singapore is seeking a qualified researcher to build and maintain end-to-end data pipelines for SCIS. You will develop and manage research codebases for optimization and ML experiments, ensuring reproducibility and scalability.

The role involves implementing optimization algorithms, creating internal tools, and collaborating with researchers to translate models into reusable, production-grade software.

Qualifications

• Bachelor's or Master's degree in CS, SE, data science, OR, or related field.
• Strong Python programming with production-quality code experience.
• Experience building data/machine learning pipelines (pandas, SQL, workflows).
• Experience implementing algorithms and running large-scale experiments.
• Familiarity with Linux, Git, and reproducible workflows (conda, Docker).
• Proficiency using AI tools to support development and experimentation.
• Strong problem-solving, communication, and collaboration skills.

Responsibilities

• Build and maintain end-to-end data pipelines for SCIS research.
• Develop and manage research codebases for optimization and ML experiments.
• Implement optimization algorithms and integrate with learning components.
• Develop internal tools (APIs, evaluation frameworks, dashboards) for workflows.
• Support large-scale experimentation with performance optimization and parallelization.
• Collaborate with researchers to translate models into reusable implementations.

Skills

Python

Data pipelines

ML experiments

Linux

Git

Testing

Modular design

Conda

Docker

Communication

Education

Bachelor's or Master's in CS / SE / Data Science / OR

Tools

Gurobi

CPLEX

OR-Tools

Pyomo

cvxpy

PyTorch

TensorFlow

JAX

MLflow

Weights & Biases

Ray

Dask

Job description

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).