Location: UK-remote
Type: Full-time
Compensation: Base + Commission
The Role
We’re looking for an experienced Machine Learning Instructor to teach, mentor, and inspire learners across our ML & MLOps tracks (including LLM applications). You’ll deliver live sessions, guide project work, and ensure every learner can build, ship, and explain production-grade ML systems.
What you’ll do
* Teach live workshops, labs, and clinics on ML, DL, and MLOps including evaluation and responsible AI.
* Coach learners 1:1 and in small groups through projects: scoping, experimentation, model selection, and iteration.
* Assess work (code reviews, presentations, and write-ups) and give actionable, timely feedback.
* Own learning outcomes: track progress, surface risks early, and intervene to keep cohorts on course.
* Evolve the curriculum: refresh content, create new labs/capstones, and incorporate real-world datasets & case studies.
* Collaborate cross-functionally with Programme, Careers, and Partner teams to align skills with hiring needs.
* Model best practice in Git, testing, CI/CD, observability, and documentation.
What you’ll teach (scope & topics)
* Core ML & DL: supervised/unsupervised learning, feature engineering, model evaluation, regularisation, tree methods, gradient boosting, neural nets, transfer learning.
* MLOps: experiment tracking, model/version management, data validation, CI/CD for ML, containerisation, orchestration, inference optimisation, monitoring & drift.
* LLM & GenAI: prompt engineering, retrieval-augmented generation (RAG), fine-tuning/LoRA, safety & eval, cost/perf trade-offs.
* Data & Platforms: Python, pandas/PySpark, SQL; production workflows on AWS/GCP/Azure; common tools (e.g., MLflow, Weights & Biases, Docker, Kubernetes, Terraform, GitHub Actions).
* Professional skills: problem framing, stakeholder communication, and written technical narratives.
Minimum qualifications
* 4+ years building and shipping ML/AI systems in industry (end-to-end ownership or major component leadership).
* Strong Python and practical ML/DL skills; confidence with at least one cloud (AWS/GCP/Azure).
* Hands-on MLOps experience (tracking, deployment, monitoring) and modern DevOps practices.
* Clear, engaging communicator with prior mentoring, teaching, or technical enablement experience.
Nice to have
* LLM app experience (RAG, evaluation, safety), vector databases, and inference optimisation.
* PySpark or distributed training; stream/data engineering basics.
* Public speaking, content creation, or open-source contributions.
* Teaching qualification or evidence of instructional design.
Success looks like
* Learners consistently meet or exceed defined skill benchmarks and ship portfolio-ready projects.
* High session engagement and satisfaction (NPS).
* Reduced intervention on delivery due to clear materials and proactive support.
Compensation & benefits
* Competitive salary with performance bonus.