Top Machine Learning Jobs in Sydney

Leads pre-sales technical execution for AI security solutions across Australia. Responsibilities include technical discovery, architecture reviews, demonstrations, proof-of-concept support, customer deployments, partner enablement, sales engineer training, demo environment management, and post-sales handoffs. Builds relationships with technical decision-makers, supports cloud and self-hosted AI deployments, and evangelizes secure agentic AI practices.

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.

SLB seeks early-career graduates for digital technology roles spanning software engineering, data engineering and analytics, artificial intelligence, machine learning, embedded systems, IIoT, high-performance computing, cybersecurity, QA automation, scientific computing, and UX design. Employees will collaborate across global technology centers to develop innovative digital solutions using cloud, data, and advanced computing technologies, supported by training and professional development.

Conduct research and experimentation in computer vision, generative AI, multimodal models, and machine learning for AI-powered video creation. Develop, adapt, evaluate, and improve models and datasets for production use, create evaluation tools, analyze model quality, and collaborate with engineering and product teams to ship scalable experiences. Document findings, monitor training or production tasks, and apply responsible AI, privacy, and security principles.

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.

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.

Drive NVIDIA’s GPU-accelerated AI ecosystem across Australia and New Zealand startups, ISVs, enterprises, and government organizations. Identify AI adoption opportunities, promote NVIDIA software, libraries, and SDKs, support developers, develop partnerships, engage influencers, attend conferences, and communicate market feedback to product teams. Collaborate with solution architects, partner managers, sales, and marketing while managing multiple projects and advancing Generative AI, LLM, robotics, and accelerated computing adoption.

Lead Neara’s Australian Grid Connections solution and government-funded GET project. Own project outcomes, customer success engineering, product priorities, partner relationships, deployment acceptance, market fit, and benefits evidence. Translate utility customer needs into requirements and acceptance criteria, guide engineering trade-offs, support demonstrations and commercial expansion, and manage senior stakeholder decisions across utilities, governments, product, engineering, sales, and delivery teams.

Supports credit, market, and liquidity risk reporting for management, ALCO, regulatory obligations, and the Board. Develops analytical models, dashboards, stress tests, and automated reporting processes; improves data quality, controls, validation, and documentation. Translates complex risk data into actionable insights while partnering with risk, business, data, and technology teams. Applies financial risk expertise, automation, machine learning, and AI within controlled analytical environments.

Sell Salesforce Agentforce and Data 360 solutions to enterprise customers by sourcing and qualifying opportunities, conducting technical discovery, crafting POVs, collaborating with cross-functional teams, and demonstrating how AI-driven customer engagement and Data 360 deliver scalable business impact. Meet and exceed technical sales targets while translating complex AI capabilities into business outcomes.

Conduct empirical economic research on AI’s effects across labor markets, productivity, inequality, industries, and macroeconomic outcomes. Build regional extensions of the Anthropic Economic Index, develop measurement methodologies, collaborate with governments, academia, industry, and civil society, and translate findings into policy recommendations. The role requires frontier econometrics, causal inference, machine learning, and structural estimation, along with research publications, stakeholder presentations, and cross-functional work improving data and measurement infrastructure.

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.

Build a self-service digital twin platform that connects machine learning algorithms with internal delivery teams. Responsibilities include designing production-grade systems, enabling users to run and correct digital twin builds, creating scalable workflow frameworks, improving tooling performance, surfacing model quality issues, incorporating human corrections into ML systems, gathering user requirements, and supporting global rollout.

Architect and build IREN’s global network automation platform, including configuration generation, deployment, validation, telemetry, observability, and closed-loop remediation. Develop production services in Python or Go, establish software engineering standards, apply AI and machine learning to network operations, ensure safe automated changes, lead incident reviews, and mentor senior engineers while setting technical direction for network software.

Leads revenue-generating consulting engagements from executive relationship development and opportunity origination through transformation delivery. Builds business cases, ROI models, and transformation roadmaps; negotiates with senior stakeholders; and owns client outcomes, quality, and profitability. Leads hybrid teams of strategists, data scientists, and delivery professionals while expanding existing client portfolios. Requires extensive consulting leadership, competitive sales, AI and data transformation experience, executive presence, and commercial accountability.

Leads technical pre-sales and post-sale engagements for geospatial imagery, 3D, and AI products. Responsibilities include customer discovery, solution architecture, tailored demonstrations, API and GIS integration guidance, onboarding, expansion opportunities, product feedback, webinars, and mentoring. The role requires strong expertise in APIs, Python, web GIS development, ArcGIS or QGIS, geospatial analysis, and customer-facing technical presentations.

Provide technical leadership for AI-powered document intelligence services, including classification, extraction, validation, transformation, redaction, and intelligent automation. Design reusable patterns for prompt engineering, structured outputs, evaluation, confidence scoring, and human-in-the-loop workflows. Move Generative AI and LLM solutions from experimentation into secure, scalable, observable production services. Influence architecture and technical strategy across teams while mentoring engineers and strengthening organizational AI and software engineering capability.

Research and develop generative, agentic AI, LLM, and reasoning-model solutions for AI-powered video creation. Responsibilities include model experimentation, training and evaluation, dataset development, quantitative analysis, responsible AI practices, and collaboration with engineering and product teams to deploy high-quality experiences at scale.

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.

Leads the ML organization developing Zendesk Custom Agents, setting strategy, managing and mentoring engineers and scientists, and delivering production generative AI systems. Oversees agent reasoning, tool use, knowledge grounding, evaluation, reliability, safety, latency, and cost optimization. Partners across Product, Engineering, Design, Security, and Science to establish quality standards, monitor production performance, and connect ML improvements to measurable customer outcomes.

Leads global AI-first MarTech engineering strategy, architecture, governance, compliance, and delivery. Oversees scalable digital platforms, public-facing properties, GenAI applications, R&D, proof-of-concepts, and automated compliance capabilities. Builds and mentors a global team of engineering and AI professionals, manages vendors, and partners with marketing, business, cloud, security, privacy, infrastructure, and legal stakeholders to deliver secure, high-performance digital experiences.

Own customer success across enterprise utility accounts by becoming a platform expert, supporting network model builds, driving adoption through training and workshops, tracking business outcomes, providing technical guidance, identifying expansion opportunities, and sharing customer insights with Sales, Deployment, and Product. The role is customer-facing, technically oriented, and focused on helping infrastructure and energy customers realize value from Neara’s AI-powered digital twin platform.

Own strategic enterprise utility accounts from delivery through adoption, measurable value, and growth. Lead complex technology programs, configure the platform to customer workflows, manage organizational change, define ROI frameworks, advise executives, and identify expansion opportunities. Serve as the internal customer advocate while bringing field insights to Product. The role requires strong technical depth, commercial judgment, executive communication, and significant experience delivering enterprise technology outcomes.

Develop and deploy production-ready machine-learning solutions for propensity modeling, personalization, growth, and retention. The role covers the full model lifecycle, including problem framing, feature engineering, experimentation, deployment, monitoring, retraining, and optimization. The Senior Data Scientist collaborates with engineers, product managers, analysts, and business stakeholders, communicates technical results clearly, improves MLOps practices, and contributes to documentation, code quality, and delivery planning.

Lead the use of data to improve business outcomes across the organization. Responsibilities include analyzing financial, operational, and growth metrics; conducting modeling and statistical analysis; delivering actionable insights; managing analytical demand; supporting strategic initiatives; and embedding scalable, automated analytics into business workflows. The role partners with executives and cross-functional teams, tracks benefits, and supports continuous improvement.