Top Machine Learning Jobs in Wellington

Join Welo Data as part of a global community focusing on data annotation and enhancing AI models with diverse linguistic expertise.

As a Software Engineer, you'll focus on backend development, DevOps, and Machine Learning, designing and maintaining cloud-based services and APIs, especially for geospatial data analysis.

Lead the definition and delivery of AI-driven features and solutions. Translate complex needs into clear requirements, prioritize use cases, and coordinate cross-functional teams to deliver effectively.

Evaluate AI-user conversations by assessing user sentiment and determining if the AI met emotional expectations, providing concise evaluations.

Lead and develop a global Site Reliability Engineering (SRE) team, ensuring system reliability and performance while driving strategic initiatives and fostering collaboration.

Develop and design learning paths and courses in various technical domains, authoring materials and assessments while collaborating with team members.

The Machine Learning Engineer role involves designing, implementing, and deploying ML solutions, focusing on LLMs and collaboration with engineering teams.

The Senior Product Manager will lead AI product strategy at CoinMarketCap, focusing on user needs and data analysis to drive product development.

The Principal Product Manager leads product lifecycle management for brand safety solutions using ML/AI, ensuring effective collaboration and communication across teams.

Deliver compelling presentations and host discussions on emerging AI topics, engaging with senior leaders to translate AI concepts into strategic insights.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

As a Software Engineer, you'll enhance the user experience of ML tools, optimize workflows, and collaborate with product managers on ML infrastructure projects.

As a Senior ML Engineer, you will operationalize ML models, design scalable MLOps infrastructure, build feature stores, automate workflows, and ensure model monitoring, collaborating with data scientists and engineers.