Top Tech Jobs & Startup Jobs in Melbourne

As a Senior Atlassian Platform Engineer, you will administer and support Jira and Confluence platforms, automate tasks, troubleshoot issues, and collaborate with various teams to enhance performance and stability.

As a DBA Engineer, you'll optimize database performance, troubleshoot issues, and handle SQL Server administration tasks, collaborating with teams to enhance data architecture and efficiency.

Lead full-lifecycle partner development: source, qualify, negotiate, align technically and commercially, launch integrations, run outreach, maintain prioritized partner pipeline, track competitors, and hand off live partnerships to affiliate managers.

The Senior Product Analyst will automate data collection, analyze product metrics, conduct A/B tests, and support product hypothesis generation.

Design and develop internal services and deployment automation tools in Go; build and maintain GitLab CI/CD pipelines; manage Kubernetes production and pre-production environments; evolve internal deployment platform (Packman); implement IaC with Terraform and Ansible; optimize ephemeral/stage environments; improve observability with Grafana/OpenSearch/Prometheus; collaborate with engineering teams to enhance developer experience and delivery speed.

Monitor and analyze product and advertising KPIs, integrate data from multiple sources, build and optimize weekly/monthly marketing reports, run A/B test analyses, create campaign summary reports, and provide actionable recommendations to improve campaign performance.

Lead and optimize engineering delivery for a mature social product: audit and improve SDLC, accelerate time-to-market, align engineering to business backlog, scale delivery frameworks (Agile/Scrum), manage cross-functional execution, and present technical strategy and risks to executives.

Lead training and fine-tuning of generative computer-vision models (text2image, image2image, video2image) including IP-adapters; develop image/video classifiers, rankers, and captioning models; track CV research and define roadmap; build iterative model improvement and quality validation systems; optimize inference performance (diffusion optimization, quantization, kernel/framework-level); optionally train multimodal CV+NLP companions.

Train and fine-tune large language models, build and maintain the full LLM stack and agentic pipelines, improve chat/agent algorithms, translate NLP research into roadmap, and collaborate with validation, content, and dataset teams to design experiments and measure model quality.