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Adjunct Software Engineering Coach (Machine Learning)

ALU

Nouveau
Senior 🇬🇧 English
Keras Scikit-learn TensorFlow Git Python Transformer models

Description du poste

About the role

ALU is recruiting an Adjunct Software Engineering Specialisation Coach for its BSc Software Engineering programme, focusing on the Machine Learning track. The role delivers the learning components of the programme in a part-time capacity of around 30 hours per week over a one-year duration, following ALU's Learning Model.

Key responsibilities

  • Manage the in-person experience, including academic orientation and induction for each intake, and develop and deliver it to quality assurance standards
  • Design, evolve and deliver the specialisation modules, and run induction and re-induction for coaches
  • Review and refine the curriculum and the operational plans per trimester based on continuous feedback
  • Audit experiential learning components and support self-directed student learning
  • Organise live coding sessions, peer learning days, mock interviews, hackathons and other student-engagement events
  • Coordinate internship modules and work with coaches, professional development teams and other stakeholders
  • Draft concept notes and plans for industry partnerships that enrich the learning content
  • Update module documentation in handbooks, policies and accreditation documents, and contribute to the annual programme review
  • Take part in student-staff consultative groups, help approve course modifications from external partners, and follow safeguarding policy

Required profile

  • Master's degree in Computer Science, Software Engineering or a related field
  • Demonstrated excellence in Machine Learning, with at least 5 years of experience mentoring or facilitating ML courses and developing ML models
  • Experience guiding students or interns on undergraduate or external projects
  • Desirable: experience teaching at higher education level, programme management, and leading a growing team

Required skills

  • Advanced linear algebra, calculus, multivariate and Bayesian probability, convolutions and other ML mathematics
  • Keras, Scikit-learn, TensorFlow and other ML libraries; Transformer models and chatbots
  • Version control (Git preferred) and Python and Linux shell scripting
  • Strong debugging, communication, problem-solving and time-management skills

Questions fréquentes

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