• Develop and refine mathematical models for complex physical systems targeting personalized medicine, ensuring their applicability to real-world experiments. • Work closely with experimental teams, translating theoretical predictions into testable hypotheses and guiding data collection/analysis. • Apply analytical and computational techniques, including simulations and numerical modeling, to study physical phenomena. • Utilize statistical physics, quantum mechanics, and applied mathematics to extract meaningful insights from experimental data. • Assist in analysing and interpreting experimental data, ensuring that theoretical expectations align with practical feasibility. • Stay up to date with the latest advancements in physics, computational modeling, and advances in spectroscopy for clinical applications. • Communicate findings effectively across disciplines, ensuring alignment between theoretical and experimental efforts. • Contribute to research publications, patents, and presentations at leading conferences.

• Support core research and quality activities, including chart abstraction. • Validate clinical data from electronic health records (EHRs) for cardiovascular clinical research studies. • Collaborate with investigators, research coordinators, project managers, and analysts to ensure data completeness, accuracy, and integrity. • Conduct source data verification and quality assurance reviews. • Identify missing or conflicting clinical information and resolve discrepancies. • Conduct literature reviews and support development of research protocols, abstracts, and manuscripts. • Abstract clinical data for quality improvement initiatives and assist with data collection for accreditation and performance improvements.

• Originate and secure new work through a proven seller-doer approach, driving revenue growth and market expansion • Lead and grow client relationships across industrial, government and resources sectors • Lead and deliver complex contaminated land projects, including high-risk, high-profile technical engagements such as auditing and expert witness work • Provide strategic environmental advice on regulatory policy, compliance frameworks, assurance and remediation strategy • Mentor, develop and lead high-performing multidisciplinary teams, fostering accountability, collaboration and technical excellence • Contribute to company strategy, future planning and regional growth initiatives across Australia and internationally

Role Description We are looking for Research Scientists and Research Engineers to help us build the data engine for frontier AI. In this role, you will bridge the gap between frontier research and production engineering. You will develop the high-fidelity environments and evaluation stacks that the world’s leading AI labs rely on to stress-test their most advanced agents. Your work will involve iterating on novel RL approaches and translating them into robust, scalable infrastructure that moves the needle on real-world model metrics. - Build Agentic Environments: Design and implement the next generation of "SimLabs", ultra-realistic, long-horizon simulation environments where agents learn to navigate ambiguity and maintain context. - Programmatic Verification: Develop rigorous, policy-aware judges and evaluations that measure genuine capability and safety beyond simple benchmarks. - Close the Loop: Design and execute high-quality post-training runs (CPT, SFT, RL) to deliver frontier performance on open-source models using curated, high-signal data. - Rapid Iteration: Debug and iterate across the full ML stack, from infrastructure to model behavior, ensuring our tools remain "command-line first" and developer-friendly. - Collaborate: Work daily with the founders and research staff to shape the roadmap and push the state-of-the-art in AI reliability. Qualifications - A Bachelor’s, Master’s, or PhD in a technical field (CS, Math, Physics, etc.), or a demonstrated "proof of work" through significant open-source contributions or industry experience. - A strong foundation in software engineering with the ability to build robust, scalable infrastructure. You should be comfortable in a Python-friendly, CLI-first development environment. - A principled understanding of foundation models, including how they are constructed, evaluated, and optimized. - Experience conducting research or technical experiments with a focus on reproducibility and data-driven results. Requirements - Research Taste: You have a strong intuition for identifying what matters in complex problem spaces. You can balance deep research exploration with the pragmatism needed to ship a product. - Impact-Driven Agency: You care about outcomes, not just activity. You don't wait for a ticket; you identify gaps in the system, build the solution, and ensure it moves real-world metrics for frontier AI labs. - Domain Expertise: Prior experience with Reinforcement Learning (RLHF/RLAIF), simulation systems, or building long-horizon agentic environments. - Proven Track Record: A history of contributing to influential ML research (e.g., publications at NeurIPS, ICLR, ICML) or maintaining high-impact open-source projects. - Post-Training Experience: Experience fine-tuning or evaluating large-scale models to deliver "frontier performance" on open-source benchmarks. Benefits - Own the Frontier: Work on the most pressing problem in AI today: making agents reliable enough for production. - High Density of Talent: Join a small, elite team where you will be pushed to do your life's work. - Elite Compensation: We offer competitive salary and equity packages to ensure we attract the best of the best. - Direct Impact: At a seed-backed startup, your work directly shapes the company's trajectory and the future of AI safety.