Top Machine Learning Jobs in New Zealand

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.

Leads the University's enterprise AI vision, strategy, roadmap, and product lifecycle. Identifies AI opportunities, develops business cases, prioritizes initiatives, and partners with data, architecture, security, and delivery teams to implement scalable, governed solutions. Engages senior stakeholders, drives adoption, evaluates value and ROI, and promotes responsible AI, data governance, security, and ethical decision-making. This is a 24-month fixed-term, full-time position.

Leads Cashalo’s 11–12-person backend and mobile engineering team while advising the Philippines GM. Owns ambiguous, cross-market business problems, drives experiments and solutions, and applies proven engineering and product patterns to regional needs. The role requires strong engineering leadership, product judgment, startup experience, AI-enabled workflows, and the ability to engage technically with Staff+ engineers. It is fully remote across Australia, Singapore, or the Philippines, with quarterly travel to the Philippines.

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.

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.

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.

Develop end-to-end machine learning pipelines and advanced models for pasture growth, biomass estimation, and farm operations. Conduct research and literature reviews, design datasets and architectures, deploy and monitor production systems, and collaborate with engineering, product and design to deliver farmer-facing insights and recommendations.

The Senior Data Scientist will enhance survey operations, analyze data quality, run experiments, and collaborate with cross-functional teams to solve business problems using advanced data science techniques.

Lead end-to-end UX and product design for Timescapes’ construction progress tracking platform. Research customer needs, prototype and test solutions, iterate throughout the product lifecycle, and design complex AI-powered capabilities. Collaborate with customers, product managers, and engineers while helping expand the design system, introduce AI-assisted workflows, and develop concepts for future products.

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.

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.

As a Machine Learning Engineer, develop ML models for computer vision, integrate with products, manage data and experiments, and work on end-to-end ML operations.

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.