Top Machine Learning Jobs in Wellington

Lead product strategy and execution for the reconciliation review experience (Auto Bank Rec). Define roadmap, success metrics, and evaluation frameworks; partner with engineering, ML, and UX to shift to management-by-exception; build dashboards and data-driven insights; and expand reconciliation into AI-native, conversational surfaces to accelerate time-to-value.

Lead the design, delivery, and operation of enterprise-scale data platforms, pipelines, and products supporting analytics, business intelligence, machine learning, and AI. Shape NZTA’s modern data platform, establish engineering standards, manage technical debt, and ensure secure, reliable, governed solutions. Work across squads and programmes while coaching data engineers, influencing technical outcomes, and contributing to continuous improvement and engineering excellence.

Lead pre-sales discovery, solution design, estimation and client workshops; define high-level application architectures, mitigate delivery risks, and incorporate AI/ML and generative AI into enterprise solutions. Produce technical proposals, estimates, and reusable consulting frameworks while guiding senior stakeholders and ensuring solutions deliver business outcomes.

Provides advanced statistical expertise to technical operations, manufacturing, quality, and regulatory teams in a GxP environment. Applies statistical modeling, multivariate analysis, machine learning, process monitoring, comparability assessments, and analytics to support product quality, investigations, validation, lifecycle management, technology transfers, and continuous improvement. Also supports regulatory submissions, audits, training, and cross-functional decision-making through evidence-based recommendations.

The Account Executive will find, win, and grow new customers in the AI/ML sector, managing complex sales cycles and relationships with stakeholders.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

Design and implement AI solutions for healthcare, focusing on foundation models and self-supervised learning. Optimize models and training pipelines for scale and rapid experimentation, evaluate generative models, collaborate with engineering and DevOps, follow test-driven agile practices, document progress, and benchmark state-of-the-art techniques to drive product development and clinical impact.