Top Machine Learning Jobs in Wellington

Conduct penetration testing across applications, APIs, networks, and systems; design breach and attack simulations; perform AI red teaming; and identify vulnerabilities through adversarial testing. Support threat modeling and secure code reviews, develop exploits and attack plans, produce remediation reports, lead client workshops, and collaborate on offensive security capability development and emerging threat research.

Leads the design, delivery, and evolution of enterprise data platforms, pipelines, and products. Provides hands-on technical leadership across squads and programs, supports analytics and AI initiatives, establishes engineering standards, and coaches data engineers. The role also oversees data governance, security, observability, automation, technical debt, and operational excellence while translating business requirements into scalable, reliable solutions.

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.

Develop data-driven solutions to complex real-world problems using data science, machine learning, statistical modeling, and analytical reasoning. Candidates should currently work in a technical data science or machine learning role at a leading technology company, hold a quantitative undergraduate degree from a top-ranked university, and reside and work in an eligible English-speaking country.

Develop, validate, debug, and optimize machine learning models, training pipelines, inference systems, and numerical implementations using Python. Build reproducible workflows, diagnose computational issues, create systematic tests, and improve memory usage, throughput, and performance. Review code and solutions, use coding agents in development, document technical findings, and deliver reliable work independently within project deadlines.

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.

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.