Software Developer 2

SOFTWARE ENGINEER / MACHINE LEARNING ENGINEER, MIT Jameel Clinic for Machine Learning in Health, to participate in the full life cycle of application and research-software development for Jameel Clinic research programs and related MIT collaborations. Responsibilities include requirements analysis, technical design, coding, testing, documentation, deployment support, troubleshooting, and production support for research workflows; developing and maintaining machine learning systems, data pipelines, model evaluation tools, applications, and reusable software components; supporting model training, evaluation, benchmarking, validation, analysis, and deployment-related workflows; working with health, biological, chemical, molecular, materials, and other scientific datasets; maintaining version-controlled repositories, testing practices, documentation, and coding standards; collaborating with faculty, researchers, students, technical staff, and external collaborators; evaluating relevant software tools, machine learning frameworks, data platforms, and engineering practices; and contributing technical material for reports, proposals, presentations, publications, and collaborator communications.

REQUIRED: Bachelor’s degree in computer science, engineering, machine learning, computational biology, computational chemistry, materials science, data science, or a related technical field; minimum five years of relevant programming, software engineering, machine learning engineering, research engineering, scientific computing, or applied data science experience; strong Python programming skills and experience with modern software development practices; experience with machine learning frameworks such as PyTorch, JAX, TensorFlow, or scikit-learn; experience developing technical design specifications, program specifications, prototypes, test scenarios, scripts, unit tests, and technical documentation; ability to work with structured and unstructured scientific datasets and translate technical requirements into maintainable code; strong analytical, debugging, documentation, communication, and collaboration skills; and ability to manage multiple technical tasks, follow through on priorities, and work both independently and as part of a team. PREFERRED: experience with molecular machine learning, biological data, chemical data, clinical data, materials data, graph-based methods, geometric deep learning, representation learning, generative models, or multimodal learning; familiarity with scientific data formats, APIs, databases, cloud computing, high-performance computing, Docker, workflow management, or MLOps practices; ability to read technical literature and translate research methods into working code; and experience contributing to collaborative research projects, open-source software, technical reports, proposals, or publications.