Top Machine Learning Jobs in Melbourne

Lead design and delivery of production-scale AI/ML products and platforms. Architect, build, deploy, monitor and improve ML systems (including LLMs, retrieval, ranking and generative AI). Drive ML lifecycle practices, data/feature foundations, evaluation frameworks and MLOps using Python, SQL and modern ML tools. Mentor engineers, set technical direction, ensure ethical, reliable, and scalable AI capabilities aligned to product priorities.

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.

Design and deploy AI/ML solutions on Snowflake, build POCs and pipelines using SQL/Python and partner tools, advise customers and integrators, enable best practices, and collaborate with product and engineering teams.

Design and implement robust, large-scale distributed ML training systems optimized for low-bandwidth, high-latency environments. Build model-parallel training strategies, checkpointing and recovery, GPU and memory optimizations, P2P networking, NAT traversal, and monitoring to ensure resilient, efficient multi-participant training.

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.

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