AI Jobs in Sydney

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Build end-to-end AI features by architecting and shipping fullstack solutions from React frontends to Python backend services that leverage voice AI and large language models to automate clinical workflows; implement and fine-tune audio processing pipelines ensuring accurate performance of Automatic Speech Recognition (ASR) and LLM agents in diverse medical environments; translate complex clinical feedback into technical solutions by rapidly prototyping and deploying improvements to model behavior, prompting strategies, and audio handling; optimize fullstack performance for real-time audio streaming and token generation to minimize latency for seamless clinician interaction; partner with implementation and clinical teams to shorten the feedback loop by shipping critical integrations and feature requests from concept to production quickly.

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As a Data Science AI Trainer, you will design original computational data science problems simulating real-world analytical workflows across various industries such as telecom, finance, government, e-commerce, and healthcare. You will create problems requiring Python programming (using libraries such as pandas, numpy, scipy, sklearn, statsmodels, matplotlib, seaborn) that are computationally intensive and cannot be solved manually within reasonable timeframes. You will develop problems involving non-trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modeling, and insight extraction. Your tasks also include creating deterministic problems with reproducible answers, basing problems on real business challenges like customer analytics, risk assessment, fraud detection, forecasting, optimization, and operational efficiency, and designing end-to-end problems covering the full data science pipeline from data ingestion through deployment considerations. You will incorporate big data processing scenarios requiring scalable computational approaches, verify solutions using Python and standard data science libraries, and document problem statements clearly with realistic business contexts along with verified correct answers.

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Contributors design graduate- and industry-level automotive engineering problems grounded in real practice; evaluate AI-generated solutions for correctness, assumptions, and engineering logic; validate analytical or numerical results using Python (NumPy, SciPy, Pandas); improve AI reasoning to align with first principles and accepted engineering standards; and apply structured scoring criteria to assess multi-step problem solving.

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Design original computational STEM problems that simulate real scientific workflows, create problems requiring Python programming to solve, ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes, develop problems requiring non-trivial reasoning chains and creative problem-solving approaches, verify solutions using Python with standard libraries (numpy, pandas, scipy, sklearn), and document problem statements clearly while providing verified correct answers.

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As a Forward Deployed AI Engineer at Lorikeet, you will act as an AI expert and evangelist for the customer base, leading AI implementation projects from kickoff to successful deployment. Your responsibilities include building and configuring workflows in Lorikeet's AI tools to meet customers' unique goals, understanding customers' businesses and recommending innovative AI solutions beyond typical customer support, working with the product team to represent customer needs and provide feedback, guiding customers through technical implementation while building strong relationships, and solving complex integration challenges creatively and efficiently.

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Design rigorous statistics problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python (NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn); improve AI reasoning to align with industry-standard logic; apply structured scoring criteria to multi-step problems.

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Create challenging coding test cases that push AI coding systems to their limits by reviewing and refining realistic coding tasks based on provided production codebases with realistic scope, requirements, and information sources. Write comprehensive functional tests that validate actual end-to-end behavior and edge cases, not just superficial checks. Craft "fair but hard" challenges where the AI has all the context it needs but must work for it, involving information scattered across files and external sources and complex reasoning. Analyze AI failures to understand what the model struggles with versus what it masters. Iterate based on feedback from expert QA reviewers who score work on seven quality criteria.

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Create challenging coding test cases to push AI coding systems to their limits by reviewing and refining realistic coding tasks based on provided production codebases with realistic scope, requirements, and information sources. Write comprehensive functional tests that validate actual end-to-end behavior and edge-cases. Craft challenges that are fair but hard, where the AI has all the context it needs, requiring complex reasoning with information scattered across files and external sources. Analyze AI failures to understand the model's struggles and strengths. Iterate based on feedback from expert QA reviewers who score work on seven quality criteria.

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Contributors may design rigorous electrical engineering problems reflecting professional practice, evaluate AI solutions for correctness, assumptions, and constraints, validate calculations or simulations using Python (NumPy, Pandas, SciPy), improve AI reasoning to align with industry-standard logic, and apply structured scoring criteria to multi-step problems.

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Contributors may design graduate- and industry-level mechanical engineering problems grounded in real practice, evaluate AI-generated solutions for correctness, assumptions, and engineering logic, validate analytical or numerical results using Python (NumPy, SciPy, Pandas), improve AI reasoning to align with first principles and accepted engineering standards, and apply structured scoring criteria to assess multi-step problem solving.