Top Machine Learning Jobs in Sydney

Develops and maintains regulatory labeling documents for pharmaceutical products across assigned markets. Prepares submission materials, performs quality checks, interprets labeling regulations, supports inspections, and responds to regulatory inquiries. Serves as a subject matter expert on labeling requirements and digital tools, contributes to process improvements and automation initiatives, analyzes labeling data, and supports digital transformation projects involving regulatory systems and processes.

Own advertiser demand revenue across ANZ by managing the full enterprise sales cycle, from outbound prospecting through complex closes. Build high-volume pipelines, multi-thread senior marketing stakeholders, manage named account territories, attend industry events, and partner with Customer Success on launches. Apply performance marketing expertise across ROAS, CPA, attribution, incrementality, and channel measurement. Collaborate with Product, Engineering, and Applied AI teams while using AI tools to improve research, sequencing, and sales operations.

Build and improve core AI and machine learning infrastructure enabling teams to develop, train, evaluate, deploy, and operate ML models and pipelines. Design scalable distributed systems, support end-to-end ML lifecycles, implement MLOps automation, collaborate with product engineering teams, lead projects from design through launch, review code, document solutions, and mentor junior engineers. Work may include dataset curation, LLM fine-tuning, model serving, monitoring, and generative AI systems.

Support development and backtesting of quantitative trading strategies: translate models to code, analyze data (including unconventional sources), and implement/monitor trading signals in live environments under senior mentorship.

Design, build, deploy, and operate agentic LLM harnesses powering Rovo AI features. Improve production model quality, reliability, and cost; develop evaluations, datasets, benchmarks, fine-tuning, and model-serving capabilities. Shape long-term LLM engineering direction, lead technical initiatives, influence stakeholders, guide virtual squads, and coach junior engineers.

Leads enterprise change governance and adoption for Data and AI products across merchandising, operations, customer, and corporate domains. Oversees change managers and vendors, establishes readiness and adoption frameworks, tracks value realization, integrates change into agile delivery, and develops enterprise AI literacy with People & Culture. Engages executives and product leaders to embed sustainable data- and AI-enabled ways of working.

Implement, configure, integrate and support InterSystems TrakCare using IRIS for Health across environments. Develop interface documentation, perform unit/system testing, manage installations/patches, troubleshoot performance, produce technical/training materials, liaise with clients and PMs, and participate in on-call and on-site activities. Provide technical guidance on networks, architectures, and data tasks.

Develop AI-driven quantitative trading strategies across traditional and on-chain markets. Responsibilities include factor mining, prediction modeling, strategy design, backtesting, portfolio construction, risk control, execution optimization, and live deployment. Build end-to-end research and trading pipelines, integrate data and low-latency execution systems, and optimize strategy performance, P&L, alpha generation, and risk across equities, futures, and cryptocurrency markets.

Lead applied AI development for pet health products, including model selection, evaluation, prompting, retrieval, fine-tuning, post-training, multimodal understanding, guardrails, and production monitoring. Build proprietary domain-specific intelligence using pet health data and veterinary expertise. Partner with product, engineering, compliance, vets, and founders to shape AI strategy, ship reliable systems, and establish the foundation for a future AI team.

Develops strategic technical partnerships with system integrators and cloud providers, creates enterprise GenAI solutions and reference architectures, supports strategic customer deals, leads technical enablement events, and channels partner feedback into Anthropic’s product roadmap. The role requires extensive customer-facing architecture experience, cloud expertise, LLM familiarity, strong communication, and the ability to work across technical and business stakeholders.

As a Software Engineer Intern, you'll create tools for trading strategies, develop research platforms, and collaborate in small teams.

Design, develop, and deploy software solutions for automated trading systems while collaborating with researchers on technology priorities.

Leads enterprise AI engineering strategy, architecture, governance, and delivery across generative AI, agentic systems, and production machine learning. Defines reference architectures, engineering standards, reusable patterns, and technical controls while assuring high-risk designs. Partners with delivery teams, Architecture, Cyber, and Risk to solve complex integration, reliability, performance, evaluation, privacy, security, and model-risk challenges. Requires extensive production AI experience, principal-level technical leadership, cloud expertise, and strong Python and modern software engineering capabilities.

Lead engineer responsible for designing, developing, and maintaining secure, scalable payments production systems. Owns production troubleshooting, automation of recurring issues, code development and review, vendor evaluations, and driving engineering best practices across teams to improve operational stability.

Provides strategic technical architecture, pre-sales, delivery, and enablement support to Salesforce partners across the ANZ region. Designs Salesforce, AI, and data solutions; advises on enterprise integrations and business use cases; develops prototypes and demos; supports RFPs and implementations; and mentors partner and internal teams. The role emphasizes Agentforce, Data Cloud, Salesforce Industries, AI adoption, solution configuration, and trusted-advisor relationship management.

Leads Western Sydney University’s enterprise AI strategy, governance, adoption, investment portfolio and enablement function. Advises executives and the Board, establishes responsible AI controls, manages multidisciplinary teams and partnerships, and scales AI and generative AI solutions from experimentation into sustainable university services. The role requires senior transformation leadership, strong understanding of AI risks and capabilities, and experience operating in complex, regulated or knowledge-intensive environments.

Unpaid, part-time internship offering hands-on mentorship in robot learning and imitation learning. Work ranges from setting up robot simulation frameworks, robot integration and human demonstration data collection, to designing and training ML algorithms and deploying models on robots. Remote theoretical work allowed; Sydney-based preferred for hardware testing.

Serve as a trusted technical advisor for AI and cloud infrastructure solutions. Partner with customers, sales teams, and technology partners to understand business needs, design architectures, lead workshops and technical evaluations, and deliver demonstrations. Translate AI strategies into scalable, secure deployments using modern infrastructure, cloud, networking, storage, GPU, container, and model-serving technologies. Present solutions to technical and executive audiences while supporting customer adoption and measurable outcomes.

Source and evaluate early-stage AI companies across Australia and New Zealand, build founder and research networks, conduct technical and commercial diligence, write investment memos, lead deals, support portfolio companies, and contribute to Airtree’s internal AI tools and evaluation frameworks.

Leads UniSuper’s data science capability and a specialist team delivering predictive models, machine learning, and advanced analytics. Sets technical standards, oversees model development and production maintenance, establishes governance, monitors performance and fairness, and translates analytical outputs into business decisions. Partners with senior stakeholders and Product Owners to identify valuable opportunities, prioritizes work, evaluates commercial and operational impact, and supports financial crime, efficiency, and regulatory objectives.

11-week PhD internship developing, back-testing and implementing quantitative trading strategies. Build and improve mathematical models, translate algorithms into code, analyze unconventional data sources, and deploy signals in live trading while collaborating with senior researchers.

Implement, test, deploy, and validate digital IoT, AI, and machine learning solutions for mining equipment. Conduct field testing, commissioning, troubleshooting, and customer-site deployments; manage digital labs; support product development, prototyping, research, and technical documentation. Apply expertise in industrial equipment, sensors, instrumentation, and condition monitoring while collaborating with technology and product teams globally.

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.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Lead and mentor engineers to design, build, and maintain scalable automation and onboarding systems. Implement AI/data-driven risk detection, collaborate with product/ops/compliance, drive architecture and reliability, and improve operational efficiency.