Top Machine Learning Jobs in Sydney

Build and deliver production-grade generative AI and LLM applications using Python, machine learning, cloud platforms, APIs, SQL, and data pipelines. Contribute to AI evaluation, observability, testing, code reviews, and technical standards. Collaborate with clients and domain experts to define solutions, explain technical concepts, and communicate risks and trade-offs. The role supports end-to-end AI/ML delivery while developing technical and consultative expertise.

Develop, test, deploy, and operate machine learning pipelines and AI products using Python, SQL, cloud platforms, and infrastructure-as-code tools. Build generative AI and traditional ML solutions, prototype proofs of concept, analyze data quality and coverage, and support dashboards and reporting. The role follows agile engineering practices, including test-driven development, continuous delivery, pair programming, and shared DevOps ownership.

Serves as the primary technical and customer support contact for complex Azure issues and projects. Troubleshoots mission-critical applications, conducts health checks and root-cause analysis, manages escalations, advises on Azure configurations and deployments, and communicates technical issues to varied audiences. Synthesizes customer feedback, identifies product and knowledge gaps, advocates for platform improvements, and partners with engineering, product, field, and support teams to improve reliability and customer experience.

Develops advanced AI and machine learning solutions, including forecasting, reinforcement learning, optimization, generative AI, conversational AI, and graph AI. Leads predictive modeling initiatives, foundation model development, AI governance, and responsible AI practices. Partners with business and technical leaders to align AI strategies with organizational goals, establish performance metrics, and deliver measurable cost savings, efficiency improvements, and revenue growth.

Provide technical and operational support for a cancer genomics lab: develop and maintain bioinformatic pipelines for scRNA-seq and spatial transcriptomics, integrate multi-omic datasets, manage and archive data, contribute to codebase improvements, communicate results, assist publications and grants, train junior staff, coordinate projects and ensure compliance and data integrity.

Customer-facing Threat Analyst supporting financial institutions with fraud detection, prevention strategy, behavioral biometrics, and advanced analytics. Responsibilities include advising customers, deploying fraud rules, measuring detection performance, researching emerging threats, delivering reporting and insights, educating prospects, supporting sales, and collaborating with Data Science and product teams. The role requires presenting to varied audiences, building customer relationships, and traveling across Australia and New Zealand for customer meetings and workshops.

Provides high-touch technical support to Databricks’ largest customers by troubleshooting complex Spark, SQL, Delta, streaming, ML, and AI issues. Performs root-cause analysis, develops proofs of concept, monitors performance, creates playbooks, trains customer teams, and partners with Field Engineering, R&D, NOC, Sales, and Product teams. Acts as a trusted technical advisor during customer engagements and business reviews.

Provide high-touch technical support to Databricks’ strategic customers by troubleshooting complex Spark, SQL, Delta, streaming, ML, and AI issues. Perform root cause analysis, build and monitor proofs of concept, develop support playbooks, train customer teams, improve support processes, and advocate for customers. Partner with Field Engineering, Sales, Product, R&D, and NOC teams during customer engagements, including onsite technical presentations and production issue resolution.

Develop threat models, statistical verification algorithms, calibrated tests, and production verification services for decentralized training and inference on untrusted heterogeneous hardware. The role focuses on detecting incorrect model outputs, malicious training behavior, free-riding, poisoning, data extraction, and reward manipulation while controlling false positives and negatives. Candidates need deep statistics and probability expertise, experience shipping or publishing calibrated decision systems, and familiarity with statistical testing, re-execution, cryptographic proofs, and trusted hardware.

The Senior Data Scientist will enhance survey operations, analyze data quality, run experiments, and collaborate with cross-functional teams to solve business problems using advanced data science techniques.

Leads end-to-end enterprise architecture for AI, data, cloud, integration, and digital initiatives. Designs AI-enabled solutions, agentic workflows, AI-ready data platforms, integration patterns, governance frameworks, and security controls. Partners with business, engineering, data, security, Microsoft, ServiceNow, and vendors to deliver strategic technology outcomes. Supports architecture assessments, solution designs, compliance reviews, roadmaps, and reusable enterprise standards.

Lead frontend architecture and complex cross-team initiatives for Canva’s Photo Editor. Set technical direction, improve performance, establish engineering standards, mentor senior engineers, identify strategic technical opportunities, and collaborate with product, design, and machine learning teams. Contribute hands-on to critical projects involving complex frontend applications and potentially graphics-intensive image-editing experiences.

Lead end-to-end UX and product design for Timescapes’ construction progress tracking platform. Research customer needs, prototype and test solutions, iterate throughout the product lifecycle, and design complex AI-powered capabilities. Collaborate with customers, product managers, and engineers while helping expand the design system, introduce AI-assisted workflows, and develop concepts for future products.

Design and deliver data science and analytics solutions for Woolworths Group retail challenges. Responsibilities include framing problems, implementing analytical workstreams, managing stakeholder relationships, translating technical insights into actionable recommendations, maintaining quality standards, and advancing analytical methodologies. The role requires strong programming, querying, statistical, machine learning, communication, and commercial problem-solving skills.

Leads Canva’s algorithmic strategy and roadmap for content intelligence, discovery, and personalization. Partners with business and engineering leaders to prioritize ML and AI opportunities, drives LLM-powered and real-time personalization experiences, and leads multidisciplinary teams across ML research, engineering, data science, and product management. Translates complex algorithmic concepts into strategic decisions for senior stakeholders while scaling high-impact machine-learning products.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

The Senior Research Officer will lead a causal target validation program in genomics, executing experimental strategies and collaborating to derive insights linking genetic variations to immune functions.

Manage day-to-day tax compliance across income tax, GST, excise, FBT, and other taxes. Research and interpret Australian taxation law, provide ad hoc tax advice, maintain tax governance procedures, and liaise with finance and business stakeholders. The role also supports tax projects, process improvements, legislative updates, and the use of SAP, robotic process automation, AI, and machine learning to improve compliance activities.

Own the end-to-end ML infrastructure supporting forecasting and other production models. Responsibilities include feature store development, model deployment, experimentation in Databricks, serving APIs, monitoring, drift detection, forecasting infrastructure, and explainability systems using LLMs and vector databases. Partner closely with data scientists, backend engineers, and product leaders to ship reliable, low-latency ML systems on AWS.

Lead the design, development, deployment, and operation of scalable AI/ML products and platforms. Own the full machine learning lifecycle, including experimentation, evaluation, inference, monitoring, and optimization. Develop LLM, retrieval, ranking, relevance, and generative AI solutions using Python, AWS, SQL, Airflow, Kafka, and BigQuery. Set technical direction, establish evaluation frameworks, improve engineering practices, mentor engineers, and promote reliable, ethical, and compliant AI/ML delivery.

Design, develop, and manage large-scale search and content recommendation systems. Apply ML-based personalization to improve content matching and distribution. Lead core recommendation module development, define success metrics, and collaborate with content, product, and business teams to drive AI product upgrades and better user and business outcomes.

Design, build, and manage large-scale search and content recommendation systems using machine learning and personalization techniques. Collaborate with content, product, and business teams to define success metrics, drive AI product improvements, and maximize content value and user impact.

Design, build, test, deploy, and monitor reliable AI/ML products and services at scale. Develop real-time and batch data pipelines, optimize ranking and retrieval models using embeddings, transformers, and LLMs, and establish evaluation, observability, and model lifecycle management practices. Collaborate across product, engineering, data, and consumer experience teams while contributing to scalable ML platform capabilities.

Lead global regulatory strategies and submissions for orthopedic medical devices and SaMD across US, EU and Australia. Manage pre-submissions, technical documentation (510(k), technical files), postmarket surveillance, CAPAs, recalls, and regulatory impact assessments. Serve as RA subject-matter expert on cross-functional teams, liaise with regulators (FDA, Notified Bodies, TGA), and ensure compliance with relevant standards and company QMS.

Build and operate end-to-end machine learning pipelines, including training, evaluation, deployment, monitoring, and model serving. Automate CI/CD, infrastructure provisioning, and reproducible environments; manage distributed GPU training across cloud and on-premises platforms; and maintain reliable, scalable inference services. Improve ML tooling, monitor production health, troubleshoot failures, and support deployment of multimodal geospatial models into customer environments.