Role: Machine Learning Instructor
Type: Contract/ Contract To Perm
Purpose: Deliver hands-on, cohort-based training that upskills experienced Data Scientists into Machine Learning Engineers.
What you’ll do
* Lead interactive, instructor-led sessions and labs that mirror real employer problems (incl. hackathons/challenges and a graduation showcase).
* Teach core MLE/MLOps skills: production-ready data engineering, pipelines, packaging, testing, CI/CD, model deployment, monitoring/observability, incident response, documentation, and version control.
* Cover cloud fundamentals (AWS/Azure/GCP), containers, and secure delivery practices appropriate to client contexts.
* Embed ethics-by-design: bias, fairness, responsible/secure use of AI, and communicating trade-offs to non-technical stakeholders.
* Provide 1:1 coaching, code reviews, and timely formative feedback; tailor support for mixed skill levels across departments.
* Help set up/operate a sandbox environment for hands-on work when department access is constrained.
* Track attendance and learner progress; run brief post-session surveys and address feedback quickly.
* Contribute to curriculum iteration between cohorts and regular knowledge-sharing with programme stakeholders.
* Supply inputs for monthly MI/reporting and participate in weekly check-ins.
What you’ll teach (examples)
* Data pipelines (batch/stream), feature stores, testing & QA for data/ML.
* Packaging & releasing ML services/APIs; infra-as-code basics; CI/CD.
* Deployment patterns (serverless, containers) and runtime monitoring (performance, drift).
* Secure data handling, documentation, and supportability in production.
About you (essential)
* Significant industry experience as an ML Engineer/MLE (or similar) delivering models to production.
* Strong Python and software engineering practices (Git, testing, code quality, packaging).
* Practical exposure to cloud, containers, and CI/CD for ML workloads.
* Proven success teaching/mentoring advanced practitioners; confident running hackathons and live coding.
* Able to explain complex concepts to non-technical audiences and work in agile, multi-disciplinary teams.
* Commitment to accessibility and inclusive delivery; responsive to learner feedback.
Nice to have
* Experience in UK government, GDS/DDaT roles & standards.
* Familiarity with model governance, interpretability, and privacy-preserving techniques.
Success looks like
* High learner satisfaction and completion; measurable progress toward DDaT Machine Learning Engineer competencies.
* Robust, accessible materials and labs that run reliably across cohorts.
* Clear MI and actionable feedback driving continuous curriculum improvement.