Role Snapshot

ANZSCO Code: 262111 — ICT Systems Analyst / Architect (AI/ML Engineer roles are typically mapped here or to 262113)
Role Variants: Machine Learning Engineer, ML Engineer, Applied AI Engineer, LLM Engineer, Applied Scientist, AI Platform Engineer, MLOps Engineer, Data Scientist (ML-focused), Computer Vision Engineer, NLP Engineer, GenAI Engineer
Parent Category: NZ Technology Roles
Skill Level: 1
Green List: Not currently listed. AI/ML Engineer is not on the NZ Green List as of mid-2026. This is an emerging shortage occupation and the classification may be reviewed. Check Immigration New Zealand for the current list before making plans.
National Occupation List (NOL): Yes — ICT occupations under ANZSCO 262111 and 262113 are on the National Occupation List, making them eligible for the Accredited Employer Work Visa (AEWV) with a qualifying job offer from an accredited employer

🇦🇺Also available for AustraliaAI / ML Engineer Roles in AustraliaCSOL listed · 482 → 186 · deeper market, higher salary ceiling→

New Zealand does not have a formal statutory registration or licensing requirement for AI and ML Engineers. This is a global skills profession: your qualifications, published work, GitHub portfolio, and production experience travel with you. The absence of a credentialing process means the job search moves faster than it does for licensed health or engineering professions, and overseas applicants compete on the same footing as local candidates from day one. The NZ market is driven primarily by software technology companies, financial services organisations, and a growing cluster of AgriTech and HealthTech employers. Most ML roles in NZ sit within product or platform teams rather than in dedicated research divisions — applied ML work is the norm, fundamental research is rare outside university settings.

• Model development and training: designing, training, and evaluating machine learning models for classification, regression, ranking, recommendation, anomaly detection, and forecasting tasks
• Large language model (LLM) work: fine-tuning foundation models, building retrieval-augmented generation (RAG) pipelines, prompt engineering, and evaluating model outputs for production use cases
• Feature engineering and data pipeline ownership: building and maintaining the data pipelines that feed model training and inference, often in collaboration with data engineers
• MLOps and model lifecycle management: deploying models to production, monitoring model performance and data drift, managing model versioning and retraining pipelines
• Model serving and API development: wrapping model inference in REST or gRPC APIs using FastAPI or similar frameworks; optimising latency and throughput for production environments
• Experimentation and evaluation frameworks: designing offline and online evaluation protocols, A/B testing infrastructure, and model performance dashboards
• Computer vision: image classification, object detection, segmentation, and video analysis for healthcare, industrial inspection, and AgriTech applications
• Natural language processing: text classification, entity recognition, summarisation, semantic search, and document understanding
• Stakeholder communication: translating model performance metrics and uncertainty into business language for non-technical audiences; increasingly expected at senior level

Typical employers: Xero (Wellington / Auckland — the largest NZ-headquartered ML employer; accounting AI, transaction categorisation, anomaly detection); Trade Me (Auckland — search, recommendation, fraud); Fisher & Paykel Healthcare (Auckland — clinical AI); NIWA (Wellington — environmental ML, climate modelling); Te Whatu Ora / Health New Zealand (various — clinical decision support, population health models); Precision Health; smaller ML consultancies (Soul Machines, Verizon Connect NZ, and others); NZ offices of global players including AWS, Microsoft, Google, and Salesforce (small ML teams). University AI labs (University of Auckland, Victoria University of Wellington, University of Otago) occasionally hire applied researchers who bridge into industry roles.

Salary Benchmark

AI/ML Engineer salaries in New Zealand have risen materially over the past two years as local demand has grown faster than the supply of experienced practitioners. Comparable positions were NZD 20,000 to 30,000 lower in 2023 and 2024. NZ salaries remain below Australian equivalents in most bands, but the gap narrows when cost of living and quality of life factors are included. Senior and staff-level ML Engineers with LLM or GenAI experience are currently the most competitive hire target in the NZ market.

Typical Ranges (NZD per year, before tax):

• Mid-level ML Engineer (3–6 years experience, strong production background): $110,000–$140,000
• Senior ML Engineer (6–10 years, technical lead capability, LLM / MLOps experience): $140,000–$175,000
• Staff / Principal ML Engineer or Applied Research Scientist: $175,000–$220,000+

These ranges reflect direct employment packages. Most NZ ML roles at mid-to-senior level include KiwiSaver contributions (employer minimum 3%), and some include equity or long-term incentive arrangements, particularly at scale-up or global-company NZ offices. Roles explicitly requiring LLM fine-tuning, RAG pipeline architecture, or production GenAI experience are currently pricing toward the top of or above these bands, reflecting the speed at which GenAI demand has moved since 2023.

A note for migrants arriving from US or UK AI organisations: NZ base salaries are lower in absolute terms than US Big Tech equivalents. Total compensation at US FAANG-equivalent companies includes equity that materially inflates the comparison. NZ roles at the senior level are competitive for the cost base, but the delta is real and should factor into your financial planning.

Source: SEEK NZ — Machine Learning Engineer | Data reviewed May 2026

Cost of living: For an independent comparison of purchasing power by city, see Numbeo — New Zealand. TEFI provides clients with a detailed financial planning workbook to model living costs, net income, and mortgage serviceability by NZ city — ask Tate for a copy.

Where Demand Is Strongest

NZ’s AI/ML employment market is heavily concentrated in Auckland and Wellington. Unlike some other technical professions where regional demand is meaningful, ML engineering roles outside these two cities are sparse. Candidates with strong remote-work track records may find opportunities with Auckland- or Wellington-based employers willing to consider distributed arrangements, but this is employer-dependent.

• Auckland — The largest concentration of ML roles in NZ. Xero’s Auckland office, Trade Me, Fisher & Paykel Healthcare, Datacom, and the NZ offices of AWS, Microsoft, Google, and Salesforce are all Auckland-based or Auckland-weighted. FinTech and InsurTech ML roles are primarily Auckland. The strongest candidate pool is here, but so is the strongest employer demand. Auckland is where most overseas ML migrants should focus their primary job search.
• Wellington — Xero’s headquarters and a cluster of govtech and data-science-adjacent ML roles. NIWA, MSD (Ministry of Social Development), StatsNZ, and Wellington-based AI consultancies generate steady demand. Wellington’s ML market is smaller than Auckland’s but more government-orientated — ML Engineers comfortable with public sector data and policy contexts find Wellington roles particularly accessible. Victoria University of Wellington also produces applied ML research that creates industry adjacencies.
• Christchurch — A smaller but growing tech cluster. AgriTech is a notable sector here, with precision agriculture, crop yield modelling, and environmental sensing generating ML demand. Lincoln Agritech, Plant & Food Research, and AgriTech start-ups are the primary employers. ML Engineers with computer vision or time-series forecasting backgrounds are better positioned for Christchurch’s sector mix.
• Hamilton / Waikato — AgriTech and dairy-sector ML applications (Fonterra, LIC Genetics, The Agriculture & Environment Research Unit) create niche ML demand. Smaller market than the main centres but less competitive for candidates targeting applied agricultural ML.

Licensing & Registration

There is no licence or statutory registration required to work as an AI/ML Engineer in New Zealand. This is a globally portable profession: your qualifications, skills, and demonstrated track record are assessed directly by employers, not by a registration board. Overseas practitioners can apply and begin work immediately upon securing a valid work visa, without any New Zealand-specific credentialing process.

What NZ employers assess instead of registration:

• Degree qualification: A bachelor’s degree or higher in Computer Science, Mathematics, Statistics, Software Engineering, or a related quantitative discipline is expected for ML Engineer roles. Master’s or PhD qualifications are common among Applied Scientist and research-adjacent roles but are not required for engineering positions. NZ employers accept degrees from recognised international universities without conversion or equivalency assessment.
• GitHub portfolio and technical work evidence: In a market without formal credentialing, a public GitHub portfolio demonstrating real model work is a significant differentiator. Employers and technical hiring managers routinely review GitHub profiles before interview. Repositories that show end-to-end pipelines — from data to training to evaluation to serving — are more useful than code-only snapshots. Research publications with model benchmarks carry similar weight, particularly for Applied Scientist-titled roles.
• Kaggle competitions and public benchmarks: While not required, Kaggle competition rankings (Master and above) or placements in public ML benchmarks are frequently cited positively in NZ hiring processes. They provide a credible external signal of modelling capability.
• English language: No formal English test is required for most applicants from predominantly English-speaking countries or for AEWV applications from most countries. IELTS or equivalent may be required for immigration purposes depending on your nationality — confirm with Immigration New Zealand.
• Professional associations: Membership of organisations such as the ACM (Association for Computing Machinery) or IEEE is respected but not required. NZ does not have a dedicated ML professional association with mandatory membership.

For Engineers with degrees from countries where NZ employers have less familiarity (some Southeast Asian, Eastern European, or African university systems), providing a brief plain-English summary of your institution’s standing and your degree programme content alongside your CV is useful preparation. TEFI helps clients frame their international qualifications for the NZ market.

Immigration Pathway

AI/ML Engineer roles (mapped under ANZSCO 262111 or 262113) are on New Zealand’s National Occupation List (NOL) but are not currently on the Green List. The Green List status may change as the shortage becomes more formally recognised, but as of mid-2026, there is no direct work-to-residence pathway from the work visa alone. Residence depends on the Skilled Migrant Category (SMC) points system.

1. Secure a job offer from an accredited NZ employer or from an employer prepared to become AEWV-accredited. Xero, major technology companies, and most larger NZ employers are already accredited or can become accredited quickly. Start-ups and smaller employers may need to initiate accreditation, which adds time.
2. Apply for an AEWV (Accredited Employer Work Visa) — the standard temporary work visa for NOL-listed occupations. The AEWV requires a qualifying job offer at or above the AEWV median wage threshold, which ICT roles at ML Engineer level typically meet comfortably.
3. Skilled Migrant Category (SMC): After working in NZ, the standard residence pathway is the SMC points system. Points are awarded for your occupation (ICT professional occupations score points), qualifications, NZ work experience, age, and other factors. Engage a licensed immigration adviser to assess your SMC points position before or shortly after arriving in NZ. ICT professionals at Skill Level 1 in shortage occupations typically have a competitive points position.
4. Permanent residence via SMC provides the same pathway to NZ citizenship as other residence visas, after five years of residence.

If the Green List classification for AI/ML Engineer roles is updated during your visa period, it is worth re-assessing your pathway with an immigration adviser. A Green List Tier 2 listing would create a more direct and faster route to residence than SMC, and the sector dynamics make this a plausible development over the medium term.

Immigration advice: TEFI does not provide immigration advice. For visa strategy, we recommend Fabien Maisonneuve at New Zealand Shores — email fabien@newzealandshores.com and mention that Tate sent you. Fabien works with skilled technology migrants and understands the AEWV and SMC points pathways for ICT professionals.

Migrant Readiness Signals

ML Engineers who move quickly and successfully through NZ hiring processes share a consistent set of preparation markers. Given that NZ employers cannot verify your experience through a registration board, demonstrated work is what carries the most weight. The signals below are calibrated to what NZ technical hiring managers — many of whom are themselves ML practitioners — look for in overseas applicants.

• A public GitHub portfolio with real model work: This is the single strongest differentiator for overseas ML Engineers in the NZ market. Repositories showing end-to-end pipelines — data ingestion, feature engineering, training, evaluation, and serving — are significantly more persuasive than a CV description of the same work. A portfolio with no public code is a gap that NZ hiring managers will notice, particularly when they are assessing overseas candidates they cannot easily call references for on short notice. Clean, well-documented repos matter more than volume.
• LLM / GenAI experience clearly articulated: From 2024 onward, LLM fine-tuning, RAG pipeline design, and production GenAI deployment experience has become the fastest-moving hire signal in NZ’s AI market. If you have this experience, it should be front-loaded on your CV and in your cover letter — not buried in a technical skills list. NZ employers are actively trying to hire this experience and will respond to it. If you do not yet have it, consider building a demonstrable project before beginning your NZ job search.
• Research publications or documented model benchmarks (where applicable): For Applied Scientist or senior ML Engineer roles, peer-reviewed publications or entries in competitive ML benchmarks provide an external credibility signal that carries significant weight with research-orientated NZ employers (university labs, NIWA, precision health companies). They demonstrate that your modelling claims have been independently validated, which is valuable when hiring managers cannot rely on a registration body for verification.
• Realistic calibration of NZ market scale: Candidates who understand before interview that NZ has fewer pure ML roles and more hybrid ML-plus-software-engineering positions come across as well-researched. Expressing genuine interest in broader-scope NZ roles — rather than seeking a direct replica of a large US or UK AI team role — resonates with NZ hiring managers. Those who show flexibility across the ML-to-software spectrum while being clear about their ML depth are more placeable in the NZ market.
• MLOps and production experience, not just notebook modelling: NZ ML teams are typically small, meaning each engineer is expected to own the full model lifecycle from training to deployment and monitoring. Candidates who can point to production model deployments — real systems that serve live traffic, not Jupyter notebooks — stand out clearly from those who have only done modelling work. Experience with MLflow, SageMaker, Vertex AI, Azure ML, or Kubeflow is specifically valued.
• Cloud platform experience (AWS, Azure, or GCP): Most NZ ML infrastructure sits on one of the three major cloud platforms. Experience deploying models on AWS SageMaker, Azure ML, or Google Vertex AI is directly applicable to NZ employer environments and should be explicit on your CV with specific services named.
• Immigration pathway clarity: Being clear that ICT ML roles are on the NOL, that the AEWV is the first visa, and that SMC is the likely residence pathway — with a rough sense of your points position — signals thorough preparation. Employers find overseas candidates with a realistic immigration plan considerably easier to onboard and less risky to sponsor.

Where to Find Roles

NZ ML Engineer roles are advertised across general job boards, company careers pages, and LinkedIn. Given the small size of the NZ AI market, LinkedIn is particularly important for networking and for finding roles before they are formally advertised — the NZ ML community is small enough that a well-positioned LinkedIn profile and direct outreach to ML leads at target companies produces results.

• SEEK NZ — Machine Learning Engineer — the primary general job board for NZ; ML Engineer, Applied Scientist, MLOps Engineer, and AI Engineer roles are advertised here; run separate searches for each variant as NZ employers use inconsistent job titles
• SEEK NZ — AI Engineer — supplementary search covering the GenAI and LLM Engineer job title variants increasingly used by NZ employers since 2024
• LinkedIn Jobs — Machine Learning Engineer, New Zealand — critical for NZ ML job search; many roles are posted here first or exclusively; also the primary channel for direct outreach to ML leads and hiring managers at target companies
• Trade Me Jobs — IT / Telecoms — NZ-specific board; some ML and data science roles appear here, particularly from NZ-headquartered employers; search “machine learning” or “data scientist” within the IT category
• Xero Careers — Xero is NZ’s largest ML employer; monitor their careers page directly for Auckland and Wellington ML roles; they do not advertise all positions on general boards
• SEEK NZ — MLOps — targeted search for MLOps Engineer and ML platform roles; smaller volume but more specific to infrastructure-side ML work
• NZ AI / ML community and meetups: Auckland AI (Meetup.com), Wellington Data Science meetup, and the NZ AI Forum are the primary professional communities for NZ ML practitioners. These communities are small enough that consistent participation — attending events, commenting in Slack groups, connecting with speakers — generates direct introductions to hiring managers. This is more effective in NZ than in larger markets.