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.

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.

Leads ML and platform engineering teams building scalable, production-grade machine learning infrastructure. Responsibilities include coaching engineers, guiding architecture and engineering trade-offs, improving MLOps maturity and observability, translating business goals into delivery plans, managing roadmaps, maintaining system health, and supporting production incidents through code review, debugging, or hands-on development. The role supports AI products including conversational GenAI, automated bank reconciliation, document capture, and cash-flow forecasting.

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.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

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.

Design and deliver scalable Databricks-based data solutions for customer analytics projects. Build production data platforms, lakes, pipelines, and ETL/ELT processes; integrate structured and unstructured data; optimize Apache Spark solutions; and work across Azure, AWS, and GCP. Translate business requirements into technical implementations while improving data reliability, performance, efficiency, and quality. Collaborate with technical and business stakeholders in a hands-on consulting environment.

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.

Develop governed, scalable analytical data foundations and trusted data products for self-service and AI-assisted analytics. Design data models, data marts, and semantic layers; translate complex business needs into actionable analytical solutions; establish analytics engineering standards; and partner with technical and business stakeholders. The role also requires technical leadership, coaching, mentoring, governance, and knowledge sharing to improve delivery quality and craft maturity across the organization.

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.

Two-year rotational software engineering program with four six-month assignments across software development, testing, firmware, AI and machine learning, DevOps, systems engineering, and product-related functions. The role involves building production applications, using AI-powered developer tools, collaborating cross-functionally, and solving technical challenges. Candidates need a relevant bachelor's or master's degree, up to three years of experience or a technical internship, strong C++ or C# skills, scripting experience, and legal authorization to work in New Zealand.

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.

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.

10-week onsite engineering internship working with engineers to develop edge-based video AI and imaging solutions, including training AI models, building proofs-of-concept for solar/battery cameras, and applied computer vision research.

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

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 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.

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.