Top Machine Learning Jobs in Sydney

Manage strategic ecommerce accounts, driving growth, product adoption, partner health, and expansion across multiple products. Coordinate product, commercial, operational, and technical stakeholders to execute account plans, mutual action plans, joint business plans, and QBRs. Build executive relationships, navigate high-stakes client conversations, use data and AI-enabled tools to identify opportunities, and improve decision-making and operating processes across concurrent workstreams.

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.

Leads research, prototyping, and maturation of automation and mechatronics technologies for future manufacturing. Designs integrated systems involving robotics, controls, sensing, machine vision, and software; conducts experiments and proof-of-concept development; evaluates feasibility, scalability, safety, reliability, and cost; and transfers production-ready technologies to NPI teams. Provides technical leadership, mentors engineers, and develops reusable automation platforms, standards, and engineering knowledge.

Analyze billions of user behavior, fund-flow, and market records using PySpark, Hive, Trino, and SQL. Build feature pipelines, investigate data anomalies, and develop and evaluate machine learning models for risk detection. Translate model performance into business metrics such as detection rate, false-positive rate, and latency while owning a project stream end to end.

Build and deploy Microsoft Power Platform automations and AI-enabled solutions, including autonomous agents, integrations, and custom Python components. The role gathers requirements, prototypes, tests, deploys, optimizes, and maintains enterprise automation and AI solutions. It collaborates with business stakeholders, technology teams, and governance functions while explaining technical concepts to non-technical audiences.

Design, develop, deploy, and support AI-powered applications and services using generative AI, machine learning, cloud platforms, APIs, and data pipelines. Translate business needs into scalable architectures, optimize AI systems, troubleshoot performance issues, and incorporate security, privacy, governance, and responsible AI practices. Collaborate with technical and business stakeholders, evaluate emerging technologies, contribute to standards, and mentor team members.

Drive high-priority strategic and operational initiatives from diagnosis through implementation. Partner with leadership, Product, Engineering, Sales, and Customer Success to unblock projects, improve processes, unlock customer value, build new business verticals, and support cross-functional execution. The role requires analytical problem-solving, strong communication, adaptability, technical fluency, and a hands-on approach to working with data, products, and engineering teams.

Summer research scholarship for currently enrolled undergraduates. Participants will contribute to computational biology and biomedical research projects involving genomics, machine learning, spatial transcriptomics, proteomics, AI evaluation, statistical genetics, and pathology imaging. Responsibilities vary by project and include data curation, database development, software pipelines, model development, biological analysis, and benchmarking. The program lasts 8–10 weeks and provides supervised research experience at Garvan Institute of Medical Research.

Owns the vision, strategy, roadmap, and delivery of AI-powered products. Leads and develops AI Product Managers and Product Owners, partners with Engineering and Design, and translates customer, market, and data insights into commercially viable solutions. The role requires strong AI/ML fluency, stakeholder influence, delivery leadership, and experience scaling AI-enabled user experiences, automation, recommendations, and decisioning workflows.

Designs end-to-end enterprise technology solutions spanning applications, APIs, data platforms, cloud environments, and external partners. Develops solution architectures, integration specifications, technology roadmaps, and reusable patterns. Collaborates with stakeholders, architects, engineers, vendors, and delivery teams to implement secure, resilient, compliant solutions. Supports hybrid integration, event-driven architectures, Azure cloud adoption, data architecture, and future AI and machine learning capabilities.

Own the end-to-end lifecycle of custom AI products, from discovery and prototyping through launch and iteration. Translate business needs and AI capabilities into product requirements, prioritize roadmaps, assess feasibility and risk, lead testing and user acceptance, measure ROI, and ensure responsible, ethical, privacy-compliant deployment. Partner with data scientists, engineers, and commercial stakeholders across Nine’s media portfolio.

Build and optimize Generative AI proofs of concept and MVPs by designing agents, prompts, RAG patterns, orchestrations, and end-to-end workflows. Translate business needs into measurable AI solutions, assess technical feasibility, integrate LLMs with APIs and enterprise connectors, and support deployment readiness. Collaborate with AI centers of excellence, product teams, IT, and business stakeholders to frame requirements, resolve blockers, and enable incremental delivery.

Designs enterprise AI solutions using machine learning, NLP, deep learning, generative AI, LLMs, RAG, and agentic frameworks. Builds scalable architectures involving cloud platforms, microservices, APIs, distributed systems, data platforms, MLOps, LLMOps, CI/CD, Docker, and Kubernetes. Translates business needs into optimized AI systems while managing stakeholder communication, cost, risk, and performance.

Develop machine learning, deep learning, generative AI, LLM, optimization, and big data solutions for consulting clients. Build predictive models, RAG pipelines, conversational assistants, autonomous agents, and simulation models supporting AI-driven digital transformation. The role requires strong statistics, quantitative analysis, Python proficiency, and collaboration with clients and consulting teams.

Conduct quantitative trading research across equities and on-chain assets, including factor discovery, predictive modeling, strategy design, backtesting, portfolio construction, risk control, and trading pipeline development. Apply machine learning and deep learning to financial time-series data, explore alpha signals, and research AI-driven trading methods in traditional and crypto markets.

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.