Top Machine Learning Jobs in Melbourne

One-year, full-time fellowship supporting design, delivery, and evaluation of interventions for smallholder farmers. Duties include research and analysis, pilot design, field implementation, M&E, and cross-team collaboration with heavy mentorship and rotating in-country placements.

The Reserve Engineer maintains and services robotic hardware, responds to failures at client sites, and ensures customer satisfaction. Responsibilities include learning hardware, communicating repairs, and assisting with deployment projects.

Lead and grow a 5-7 person enterprise sales team across ANZ, drive new logo acquisition and account expansion, develop GTM and business plans, manage full sales cycle, recruit and coach future sales leaders, and exceed quarterly and annual targets.

The Head of Data will lead the data organization, define the company-wide data strategy, ensure high-quality decision making, collaborate across teams, mentor leaders, and establish best practices in data governance and execution.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

The role involves analyzing biological mass spectrometry data, developing software tools, and scaling workflows for AI and human consumption in drug safety assessment.

Manage requests for new ServiceNow reports and dashboards, resolve dashboard/reporting incidents, train users on dashboards, build executive KPIs and scorecards, engage stakeholders to gather requirements, and apply AI/Generative AI for reporting automation and insights.

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

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

Design and implement ML infrastructure for scalable deployment, collaborate across teams, optimize ML systems, and enhance tooling.

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

Design, build and deploy ML models and production ML services (real-time and batch), develop MLOps pipelines, feature engineering, LLM and generative AI applications, monitor and optimise models, and collaborate cross-functionally to deliver measurable business impact.

Design, train, and deploy machine learning models focused on customer experience and fraud detection. Work with large datasets, optimize model performance, implement real-time scalable deployments, and collaborate with fraud, product, and engineering teams while applying MLOps and CI/CD best practices.

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.

Ship applied research to improve conversational voice AI: fine-tune and evaluate LLMs for real-time phone workflows, build evaluation frameworks, analyze production conversations, curate and generate training data, investigate regressions, and collaborate with engineering to deploy robust, low-latency models.

The Senior Machine Learning Engineer will design, train, and deploy ML models for optimizing revenue in advertising, taking ownership of product development and leading the ML discipline.