Top Machine Learning Jobs in New Zealand

Lead end-to-end development of predictive machine learning systems for agricultural operations, including modeling, dataset construction, training, deployment, monitoring, and production support. Conduct research across machine learning and pasture science, work with complex high-volume datasets, and translate academic research into practical systems. Collaborate across engineering, product, and design while developing innovative models for pasture growth, animal health, remote sensing, and grazing optimization.

Build and improve core AI and machine learning infrastructure enabling teams to develop, train, evaluate, deploy, and operate ML models and pipelines. Design scalable distributed systems, support end-to-end ML lifecycles, implement MLOps automation, collaborate with product engineering teams, lead projects from design through launch, review code, document solutions, and mentor junior engineers. Work may include dataset curation, LLM fine-tuning, model serving, monitoring, and generative AI systems.

Lead the technical direction of Watercare’s enterprise data and analytics capability. Establish standards and reference patterns, design and assure complex solutions, review code and releases, resolve platform and pipeline issues, and contribute hands-on to data products, models, dashboards, and pipelines. Coach multidisciplinary teams, support governance and scalability, and communicate technical trade-offs to technical and non-technical stakeholders.

Leads enterprise-wide fraud prevention, detection, investigation, disputes, and resolution operations across card and consumer loan portfolios. Oversees 250+ employees, multiple Directors, a $20M+ budget, fraud technology platforms, regulatory compliance, vendor relationships, and strategic initiatives. Presents fraud performance to executives and board committees, manages risk appetite, drives organizational development, and partners with analytics and IT teams on AI, machine learning, authorization systems, and digital transformation.

Design, develop, maintain, document, and integrate AI and machine learning solutions supporting AA staff and customers. Collaborate cross-functionally to translate business needs into practical systems, apply software engineering best practices, and contribute to safe, innovative AI initiatives.

Leads end-to-end data science and AI/ML initiatives, including problem formulation, experimentation, model deployment, evaluation, optimization, and production monitoring. Develops prescriptive AI solutions, foundation-model and agentic-system evaluation frameworks, responsible-AI controls, RAG and knowledge-graph integrations, and model telemetry. Partners cross-functionally to improve business metrics, translate technical outputs into value, and mentor junior data scientists.

Design and develop end-to-end IoT solutions connecting devices, embedded systems, data platforms, cloud services, and APIs. Responsibilities include implementing communication protocols, building secure architectures, developing data pipelines, testing and troubleshooting systems, improving reliability, evaluating emerging technologies, and collaborating with hardware, software, engineering, and product teams.

Identify patentable inventions across firmware, software, machine learning, electronics, and mechanical systems; draft patent specifications and claims; manage prosecution; lead freedom-to-operate and invalidity work; and develop AI-enhanced intellectual property tools. The role partners closely with engineers, analyzes design documents and code, identifies patent whitespace, and helps shape scalable IP processes for Halter’s rapidly expanding technology portfolio.

Leads credit risk analytics for card portfolios, including executive reporting, early warning indicators, acquisition-channel analysis, portfolio performance, AI innovation, automation, and credit loss forecasting. Develops vintage and roll-rate models, translates complex data into recommendations for senior stakeholders, evaluates emerging technologies, and mentors junior analysts. Requires strong quantitative, statistical, communication, and data storytelling skills, with proficiency in SQL, Python, SAS, Excel, and BI tools.

Designs and delivers technical demonstrations, proof-of-concepts, architectures, prototypes, and custom applications for life sciences customers. The role addresses drug discovery, clinical analytics, regulatory workflows, omics processing, governance, reproducibility, and validation requirements. It partners with account executives, Customer Success, and Solutions Architects to convert evaluations into production deployments while building reusable technical assets and identifying new customer use cases.

Develop and design software across ERP, APIs, integrations, mobile applications, and web solutions. Lead technical implementation decisions, mentor developers, contribute to architecture, refine backlogs, identify risks and dependencies, support sprint planning, and promote secure, maintainable engineering practices. The role also contributes to cloud deployment, DevOps improvements, technical product decisions, and adoption of AI-assisted development tools.

Lead data science initiatives involving exploratory analysis, statistical and machine learning model development, experimentation, optimization, and business metric improvement. Design and deploy personalization, recommendation, and ranking systems across digital channels, addressing cold-start and large-catalog challenges. Collaborate with engineering and stakeholders to translate business needs into scalable technical solutions, drive adoption of data products, and mentor junior data scientists.

Designs, builds, deploys, and supports AWS cloud environments for diverse customers. The role focuses on infrastructure automation, Infrastructure as Code, DevOps, observability, security governance, incident and problem management, cloud optimization, and continuous service improvement. Responsibilities include advising customers, enabling cloud adoption, supporting mission-critical production environments, contributing to platform strategy, and participating in an after-hours on-call roster.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Manages ML Ops engineers and leads development, deployment, and operation of production machine learning systems. Owns ML engineering and data science tooling roadmaps, scalable cloud infrastructure, ETL pipelines, model serving, monitoring, testing, and CI/CD practices. Provides technical leadership to data science managers, coaches engineers, establishes best practices, and drives reliable ML lifecycle implementation using Google Cloud technologies and distributed computing platforms.

Provide agricultural science expertise to shape predictive grazing systems: translate biology into testable hypotheses, design and run experiments, validate and interpret models, define measurement protocols, identify new datasets and sensors, and partner with ML engineers, product and data teams to improve model performance and product value for farmers.

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Own end-to-end development of advanced ML systems for agricultural applications: research, model design, dataset construction, training, deployment, and production monitoring. Collaborate across engineering, product, and design to translate cutting-edge research into practical predictive products for pasture and animal management.

Lead pre-sales engagements for Provenir's decisioning platform: conduct customer discovery, design solution architectures, build tailored demos, respond to RFPs, collaborate with Sales, Data Science, and Professional Services, and serve as subject matter expert in credit risk, fraud, and financial crime to drive deals and ensure smooth delivery handoffs.

Provide technology and digital legal advice on procurement, outsourcing, IP, privacy/data protection, cybersecurity incident response, AI/biometric/blockchain projects, commercial negotiations, M&A of tech assets, and IT disputes for complex, cross-border transactions.

Lead design and implementation of large-scale distributed production systems powering generative AI features. Own architecture, mentor engineers, manage technical debt, operationalize ML/LLM models, and collaborate with scientists to productionize research using Python, SQL, Spark/Dask, AWS, and Kubernetes.

Work with the Applied ML team to prototype, evaluate, and ship ML and algorithmic solutions for vehicle and parts problems. Contribute to modelling, ranking, retrieval, evaluation datasets, and production-ready systems while learning engineering best practices and collaborating with product and engineering teams.

Design, develop, and deploy custom ML models and transformer-based NLP systems. Build and maintain ML pipelines, fine-tune LLMs, implement RAG, monitor production performance, research new techniques, and mentor junior engineers.