Machine Learning Instructor - Apprenticeships
2 days ago
Liverpool
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.