AI Jobs in Sydney

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As an AI Infrastructure Engineer at Together, the responsibilities include participating in on-call rotation to respond to production incidents, building and running infrastructure using Ansible, Terraform, and Kubernetes to support scaling to a large number of concurrent users, building monitoring systems to ensure high-quality service, designing and implementing operational processes such as deployments and upgrades, debugging production issues across all services and stack levels, identifying improvements for product architecture in terms of reliability, performance, and availability, and planning the growth of Together AI's infrastructure.

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Translate cutting-edge research into production-ready machine learning systems. Design, build, and deploy end-to-end ML models and pipelines. Develop and optimize models for image and video processing. Own the full ML lifecycle including experimentation, training/fine-tuning, evaluation, and deployment. Rapidly prototype using open-source models and adapt them for product needs. Conduct experiments, analyze results, and iterate to improve performance. Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale. Participate with advancements in machine learning and apply them to continuously improve products.

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Lead research that advances Maincode's work on capable, useful, and trustworthy AI systems. Design and execute experiments, develop new research directions, and collaborate closely with researchers and engineers. Produce research outputs suitable for top-tier conferences, journals, technical reports, open-source releases, or deployment in Matilda and future Maincode systems.

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Utilize proprietary software to provide accurate input and labels for healthcare and administration projects, ensuring high-quality data for AI model training. Deliver curated, high-quality data for scenarios involving patient care coordination, medical billing, administrative workflows, and healthcare operations. Collaborate with technical staff to support the training of new AI tasks and contribute to the development of innovative technologies. Assist in designing and improving efficient annotation tools tailored for healthcare and administration data. Select and analyze complex problems in healthcare and administration fields aligned with your expertise to enhance AI model performance. Interpret, analyze, and execute tasks based on evolving instructions, maintaining precision and adaptability.

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Lead and develop a high-performing team of 6 to 8 engineers, supporting performance, coaching, and day-to-day execution in a high-trust environment while shaping a culture of truth-seeking, ownership, and continuous improvement. Contribute hands-on technical leadership through design reviews, technical planning, code review, bug fixing, and selected feature development. Help the team solve ambiguous, high-leverage technical problems in AI systems and product infrastructure. Create clarity in fast-moving situations by driving strong execution, thoughtful trade-offs, and high engineering standards. Handle tough conversations with empathy and conviction, including feedback, alignment challenges, and expectation setting. Hire bar-raising candidates by partnering with Talent Acquisition to execute hiring plans and close strong engineers. Partner with Product, Design, and GTM as an equal and bring options to the executive team.

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Contributors may generate prompts that challenge AI, evaluate AI-generated solutions for correctness, assumptions, and logic, improve AI reasoning to align with first principles and accepted standards, and apply structured scoring criteria to assess multi-step problem solving.

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Design original optics problems that simulate real physics research workflows; ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes; develop problems requiring non-trivial reasoning chains in mechanics, electromagnetism, thermodynamics, and quantum mechanics; base problems on real research challenges or practical applications from optics and physics practice; document problem statements clearly and provide verified correct answers.

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The Partner AI Deployment Engineer role involves serving as the primary technical counterpart to AWS field leadership, shaping strategy, defining engagement models, and building repeatable systems that scale across AWS globally. Responsibilities include influencing joint account strategy and technical direction for high-priority opportunities, shaping how OpenAI engages with AWS, identifying new opportunities within the AWS ecosystem, and leading technical strategy for large and complex enterprise engagements. The engineer guides AWS and partner teams from ideation through architecture design, prototyping, and production deployment while acting as a technical decision-maker and escalation point. They design and communicate end-to-end AI architectures using OpenAI and AWS services, build prototypes and reference implementations, and establish best practices for scalable and secure GenAI systems. The role includes enabling AWS and partners through workshops, playbooks, and reusable solution patterns, mentoring partner technical teams to accelerate self-sufficiency, scaling impact through GSIs, RSIs, and ISVs, and closely partnering with alliances, product, engineering, GTM, and enablement teams to align strategy and execution. Additionally, the engineer contributes to internal knowledge systems and helps define standards and playbooks for the AI Deployment Engineering function.

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As the first US based Forward Deployed Engineer at Haast, you will act as the technical bridge between the product vision and customer needs. You will work as a full-stack engineer responsible for designing, architecting, and shipping full-stack features that solve customers' compliance challenges. Your role involves ownership of end-to-end technical decisions, including designing systems, shipping to production, and iterating on features based on direct customer interaction. You will maintain the technical relationship with key customers by implementing solutions, gathering requirements, and translating feedback into product improvements. Additionally, you will build scalable services and APIs for the LLM compliance platform with a focus on customer experience, make high-impact technical decisions aligned with engineering standards and customer needs, question assumptions about product development, and influence the product roadmap and engineering practices as the company scales from Series A to market leadership.

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Lead the design and implementation of complex, production-grade systems that underpin the platform at scale. Provide architectural guidance and cross-team technical leadership to ensure consistency, reliability, and long-term maintainability. Own significant platform surface areas end-to-end—from system design through to deployment, observability, and iteration. Partner with product, go-to-market, and engineering leadership to translate customer and business requirements into clear technical strategies. Mentor and level-up engineers within and beyond the immediate team, fostering a culture of technical excellence and ownership. Shape engineering standards, processes, and best practices as the organisation scales.