Responsibilities:

• Develop and deploy machine learning models that will have a profound impact on our product(s) and end users
• Analyze large datasets to identify trends and patterns
• Work across a range of use cases to improve the quality of LLM output chains
• Plan and implement MLOps, including benchmark monitoring for quality assurance and compliance
• Provide strategic input for long-term data initiatives, including data governance and ethical AI practices
• Work closely with engineers and stakeholders to implement innovative solutions
• Mentor junior data scientists

Requirements & Skills:

• Relevant tertiary qualification, Doctorate or Master’s in Data Science, Mathematics, Statistics, Computer Science, or related field, AND 4+ years industry working experience
• Strong demonstrated history of data science experience and can concisely share examples of successful past projects
• Ideally, you bring commercial experience with NPL techniques (supervised NLP learning with LSTM/CNN/Transformers and/or topic modelling), and;
• Commercial experience with GPT (langchain, llama index, RAG, multi-agent framework).
• Advanced SQL and 2+ years’ programming experience in Python and Natural Language Processing
• Excellent communicator with stakeholders at all levels
• Empathetic, collaborative, open to feedback
• Able to read and keep abreast of LLM-related research
• Able to thrive in an agile environment
• A Master’s/Doctorate in a relevant discipline is a plus
• Able to attend our beautiful Sydney CBD office 2 days a week

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Responsibilities:

• Design and implement robust pipelines to deploy machine learning models into production environments.
• Build and optimize scalable infrastructure for machine learning workflows, including data preprocessing, model training, and inference.
• Optimize models for latency, accuracy, and efficiency in real-world scenarios.
• Work closely with data scientists to transition research models into production-grade solutions.
• Develop and maintain automated workflows for version control, model monitoring, and retraining.
• Collaborate with data engineering teams to ensure efficient data pipelines and availability of high-quality datasets.
• Build tools to streamline machine learning development and deployment processes.
• Ensure deployed solutions adhere to responsible AI principles, focusing on safety.

Requirements & Skills:

• Bachelor’s or Master’s degree in Computer Science, Data Science, or AI-related fields
• Proficiency in Python and frameworks such as Tensorflow, PyTorch, or Scikit-learn.
• Experience deploying ML models in cloud environments like AWS, GCP, or Azure
• Familiarity with containerization tools (e.g., Docker) and orchestration frameworks (e.g., Kubernetes)
• Expertise in MLOps tools and practices, including CI/CD pipelines, model versioning, and monitoring
• Strong understanding of distributed systems and scaling machine learning workflows
• Solid knowledge of machine learning algorithms, model training, and evaluation techniques
• Experience working with NLP, computer vision, or generative AI models is a plus
• Ability to work in cross-functional teams and communicate effectively with data scientists, engineers, and business stakeholders.
• Strong debugging and problem-solving skills to address technical challenges in deployment environments.

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Responsibilities:

• Design, develop, and implement API integrations with Gen AI technologies such as GPT, Gemini, and other relevant platforms to enhance business applications.
• Provide technical expertise in AI concepts, including NLP, Deep learning, and LLMs. Ability to tune/train LLM Models and leverage techniques to optimize outputs for relevance, completeness, and quality.
• Create evaluation frameworks to determine model accuracy.
• Provide regular model accuracy reports.
• Collaborate with product managers, data scientists, and software engineers to develop AI-driven solutions that meet business requirements.
• Optimize the performance of AI models and integrations to ensure efficiency, scalability, and reliability.
• Stay up-to-date with the latest advancements in AI technologies and recommend innovative solutions to improve existing systems.
• Create and maintain comprehensive documentation for AI models, API integrations, and technical processes.
• Identify and resolve technical issues related to AI integrations and model performance.
• Provide training and support to internal teams on the use and implementation of AI technologies.
• Ensure that Personal Information and security policies are respected when working with training data, if any.

Requirements & Skills:

• Education: Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related field.
• Experience: Minimum 2 years of experience in AI development
• Proficiency in programming languages such as Python
• Strong understanding of NLP, LLM concepts, and Gen AI models implementation, deployment, and evaluation.
• Strong understanding of MLOPs concepts such as continuous training, continuous monitoring, and continuous improvements to machine learning and AI systems
• Hands-on experience with Gen AI technologies such as GPT, Gemini, Claude, BERT, etc.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Certifications in AI/ML, cloud platforms, and related areas.
• Experience with meta-learning, few-shot learning, or prompt engineering for LLMs
• Knowledge of RESTful APIs and web services.
• Experience with Agentic AI and designing and building solutions is also preferred
• Have deployed Gen AI systems into production.

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Requirements & Skills:

• You should be a pro in Python
• You should have hands-on working knowledge of AI frameworks such as PyTorch/TensorFlow, Transformers, CV2, and HuggingFace
• You should have hands-on working knowledge, training, and prediction of NLP models such as Spacy, GliNER, and Reading Comprehension QA models
• You should have hands-on working knowledge of common Python/AI libraries such as torch, numpy, nltk, pillow, etc. Working knowledge of Computer Vision concepts and Vision and Sentence Encoder/Decoder models is a BIG plus
• You should care about Performance, High availability, and Scalability will get to work on building key elements of a Knowledge Analytics solution, including the state-of-the-art AI models encompassing Computer Vision, Natural Language Processing, and Machine learning technologies
• Ontology-based domain modeling: Processing data with Knowledge Graph
• Microservice-based architecture
• Agile Software Development

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Responsibilities:

• Conducting experiments with LLMs: Explore and analyze the effectiveness of different architectures and techniques (SFT, RLHF, Adapters, etc.) to enhance the capabilities of AI models.
• Developing and implementing evaluation methodologies: Implement and maintain robust frameworks to assess the performance, accuracy, and user satisfaction of AI bots, including offline and online metrics.
• Optimizing models for inference: Improve the efficiency and speed of AI models during inference to ensure they meet the performance and scalability requirements for production environments.
• Collaborating with cross-functional teams: Work closely with data scientists, software engineers, and product managers to integrate AI solutions into the overall product pipeline.

Requirements & Skills:

• Proficiency in Python, algorithms, and mathematics: Strong coding skills in Python and a solid foundation in algorithms, linear algebra, probability, and statistics;
• Deep understanding of ML and DL principles: Comprehensive knowledge of machine learning and deep learning concepts, including supervised, unsupervised, and reinforcement learning;
• Comprehensive expertise in NLP and LLM: In-depth experience with natural language processing and large language models;
• Familiarity with ML frameworks and tools: numpy, pandas, scipy, scikit-learn, pytorch, transformers;
• English level: B2+.

Nice to have:

• Experience in distributed training: Ability to implement and manage distributed learning workflows, ensuring efficient parallel training of models across multiple GPUs and nodes;
• Relevant publications: Demonstrated expertise through publications in reputable conferences or journals, showcasing contributions to the field of machine learning and AI;
• Knowledge of cloud platforms: Hands-on experience with cloud services like AWS, GCP, or Azure for training DL models.

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Responsibilities:

Research Design and Implementation

• Designing Research Studies: Develop detailed research protocols and experimental designs related to natural language processing, machine learning, large language models, and multi-modal vision-language models. Ensure that methodologies are scientifically rigorous and appropriate for the research questions and include detailed analytic & results dissemination plans.
• Conducting Research: Carry out protocols, experiments, surveys, and assessments according to the designed protocols. This includes IRB documents, data extraction, recruiting participants, conducting assessments, staff training, and leading the team to deliver the research goals.
• Data Management: Support the development and maintenance of databases for research data and/or the development and integration of data collection tools. Ensure data is stored securely and is accessible for analysis.
• Data Collection and Analysis: Oversee the collection of research data, ensuring accuracy and reliability. Use statistical and data analysis software to perform complex analyses. Appropriately apply quantitative and qualitative methods to interpret data trends, correlations, and patterns. Produce actionable insights, draw meaningful conclusions, and develop recommendations based on data analysis.
• Reporting Results: Prepare visualizations, summary reports, journal publications, and presentations to communicate findings effectively. Share results with the research team and external stakeholders to advance the field.
• Conducting Literature Reviews: Regularly review current literature related to aging, cognitive health, and wellness. Summarize and synthesize findings to inform ongoing and future research projects.
• Integrating New Knowledge: Apply insights from recent research to refine hypotheses, adjust methodologies, and enhance the relevance of research projects. Stay informed about emerging trends and technological advancements.

Outreach and Networking

• Community Engagement: Participate in the development and implementation of community outreach activities to promote research and foster public understanding of aging and wellness topics.
• Professional Networking & Stakeholder Engagement: Help organize and/or attend professional meetings, networking, and stakeholder events to build relationships with research stakeholders, including academic, industry, clinical, and community partners, to facilitate research and translate/integrate research into real-world settings.
• Team Collaboration: Work collaboratively with interdisciplinary teams, including other researchers, clinicians, and specialists. Participate in regular team meetings to discuss progress, challenges, and strategies.
• Scientific Communication: Prepare and deliver presentations of research findings at conferences, workshops, and meetings. Contribute to writing and revising peer-reviewed publications and grant proposals.
• Provide service to the university and profession: participate in department, university, and professional organizations at the local and national levels.

Grant Writing and Funding

• Proposal Development: Assist in drafting grant proposals, including writing sections related to research aims, methodology, and expected outcomes. Collaborate with team members to develop comprehensive and competitive proposals for a wide variety of funders, including government, industry, and non-profit groups.
• Funding Acquisition: Help identify potential funding sources and opportunities. Contribute to the development of project budgets and funding requests.

Training and Supervision

• Mentoring: Provide guidance and support to junior researchers, students, interns, or lab assistants. Assist with training new team members and learners in areas of expertise and/or on research protocols and techniques.
• Supervision: Oversee day-to-day activities of support staff or students involved in research projects, ensuring tasks are completed effectively.

Ethical and Regulatory Compliance

• Compliance Monitoring: Participate in developing and executing compliance monitoring plans.  Provide oversight to ensure that all research activities adhere to institutional, ethical, and regulatory guidelines. Obtain necessary approvals from ethics review boards and regulatory bodies.
• Participant Confidentiality: Maintain confidentiality and privacy of research participants. Follow protocols for data security and ethical handling of sensitive information.

Requirements & Skills:

• Specific requirements for the positions include: (1) an earned doctorate in a computer science, biomedical informatics and/or other related field,  and (2) a minimum of 3 years of research experience specifically focused in an area related to natural language processing, machine learning, large language models, multi-modal vision-language models, and their applications in health outcomes.
• Experience with developing natural language processing models, especially large language models, and multi-modal vision-language models in the healthcare context.
• Outstanding oral and written communication skills, ability, and experience authoring scientific journal publications and/or technical reports on high-impact biomedical informatics journals and conference proceedings.
• Expertise in deep learning software packages (e.g., PyTorch, TensorFlow).
• Interest, ability, and experience in the design and execution of collaborative multi-disciplinary research.
• Capacity to contribute to the design of research projects and the writing of research proposals.
• Interest in and ability to translate research findings into practice, using data collected and analyzed to inform the development of health innovations, programs, and/or policies.
• Capacity to successfully interact with a variety of project stakeholders, including community leaders, study sponsors, other scientists, health care providers, policy makers, and community members.
• Demonstrated talent for collaboration and team building and ability to build and maintain effective, trusting working relationships that reflect kindness, mutual respect, and honesty when working with varied perspectives among university students, staff, and internal and external partners.
• Ability to cope with change, to shift gears comfortably, and to handle risk and uncertainty.
• Strong program development and evaluation skills required for outcomes and/or program evaluation tracking, outcome assessment, quality assurance, and service utilization review.
• Understanding of the principles of human research protection standards, including Institutional Review Board protocols and procedures
• Up-to-date knowledge of scientific and regulatory requirements for human subject research.
• Capacity to successfully manage diverse research projects and demonstrated leadership skills in guiding individuals and groups through research, assessment, and strategic planning processes.
• A strong background with monitoring the progress of research activities, and the capacity to develop and maintain records of research activities, prepare periodic and ad hoc reports, as required by investigators, administrators, funding agencies, and/or regulatory bodies.
• Ability to implement quality control processes throughout research activities.
• Effective communication, writing, critical thinking, and organizational skills are required to develop and maintain policies, procedures, and clinical delivery systems for the agency and divisional/university reports.

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