Top Machine Learning Jobs in Sydney

Own the end-to-end finance and accounting function across entities and jurisdictions. Responsibilities include financial close, consolidations, reporting, cash management, compliance, tax, audits, external partners, finance systems, controls, automation, budgeting, forecasting, fundraising support, and team leadership. The role will build scalable finance infrastructure, improve operational processes, provide decision support to executives and investors, and ensure accurate, timely, and reliable financial information.

Build, evaluate, and ship machine learning ranking and relevance systems for private, team, and brand content search. Improve relevance, recall, result understanding, search recovery, and exact title matching. Collaborate with backend engineers on ElasticSearch, OpenSearch, and vector search, extend capabilities to agent-driven search, investigate production regressions, and own scoped projects from problem framing through measurable delivery.

Leads public-sector sales and account development across Australia, New Zealand, and selected APAC markets. Builds executive relationships, develops account and capture strategies, drives complex technology opportunities through negotiation and closure, and supports post-sale adoption and expansion. Coordinates cross-functional teams and partners while navigating procurement, security, privacy, data residency, compliance, and sovereignty requirements. Represents OpenAI in government and industry forums and maintains forecasting, pipeline, customer outcomes, and executive reporting.

Own TAL’s AI product vision, roadmap, investment prioritization, adoption, and value realization. Partner with cross-functional teams to turn emerging AI capabilities into scalable products supporting claims, underwriting, legal, and customer operations. Embed responsible AI governance, privacy, security, ethics, and compliance throughout the product lifecycle while aligning stakeholders and delivering measurable customer and business outcomes.

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.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

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.

Develop full-stack web interfaces and backend architecture for neurotechnology applications; implement neural signal processing pipelines (EEG), integrate machine learning models for neurophysiological data analysis, maintain acquisition/processing software, participate in code reviews, document technical processes, and work independently within a small, research-driven startup.

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.

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.

Leads AbbVie’s ANZ data and AI strategy, overseeing analytics, data science, governance, market intelligence, reporting, data quality, and machine learning initiatives. Builds high-performing teams, drives data literacy and self-service analytics, ensures regulatory compliance, manages budgets and vendors, and partners with commercial, technology, and global teams. The role translates data and AI capabilities into customer engagement, operational efficiency, commercial insights, and measurable business outcomes.

Leads the recovery and resolution of major technology and television incidents across Sky’s complex service environment. Coordinates technical teams, vendors, executives, and partners; communicates incident status; drives workarounds, root-cause mitigation, post-incident reporting, and service improvement plans. The role also supports launches, changes, disaster recovery testing, automation, reporting, and continual service improvement while participating in a 24/7 out-of-hours rota and weekly hybrid shifts.

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.

Lead AI strategy for Productivity, identify high-value ML opportunities, toolify functionality for LLMs/agents, set technical patterns, unblock cross-team challenges, mentor engineers, partner with product/design, and ship scalable AI features measured by real user impact.

Provides strategic cybersecurity advisory and technical leadership across enterprise security assurance engagements. Leads security assessments, risk reviews, governance improvements, and security-by-design initiatives; develops standards, procedures, guidance, templates, and playbooks; advises technical and business stakeholders on controls and risk treatment; promotes security awareness; and uses metrics to improve cybersecurity processes and stakeholder outcomes.

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.

Manage and coordinate Oncofetal Ecosystem Lab projects: develop project plans, track milestones/budgets, support ethics/IRB and reporting, liaise with stakeholders, assist grant writing, and perform hands-on lab work as required.

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.

Lead advanced analytics and fraud detection initiatives within the Financial Crimes domain. Develop, deploy, monitor, retrain, and govern machine learning models using large datasets and production-grade MLOps practices. Translate analytical findings into business recommendations, collaborate with Product, Technology, Risk, Fraud Operations, and Engineering teams, evaluate emerging technologies, and mentor data scientists.