Machine Learning Instructor - Apprenticeships

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.