• 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.

Role Description Astera Institute is seeking a Research Scientist in Computational Neuroscience to develop computational models of neural circuits and brain function. - Read, analyze, and synthesize neuroscience, cognitive science, and computational neuroscience literature. - Develop biologically plausible computational models of neural circuits, brain regions, and large-scale brain systems. - Compare model predictions with experimental data and refine models accordingly. - Develop novel algorithms inspired by biological neural computation. - Analyze large-scale neural and behavioral datasets to inform and evaluate computational models. - Stay current with advances in neuroscience, machine learning, and computational modeling. - Propose and pursue original research directions that advance our understanding of brain computation. - Make novel scientific contributions to computational neuroscience through the development of new theories and models. - Document research findings and communicate results through technical reports, publications, and presentations. Qualifications - Experience programming in Python. - Familiarity with scientific computing libraries such as Jax, Pytorch, NumPy, SciPy, or similar tools. - Research experience in systems, computational, and theoretical neuroscience. - Experience working with data analysis, statistics, or machine learning workflows. - Ability to work independently and communicate technical findings clearly. Requirements - Experience with machine learning or dynamical systems modeling. - C++ programming experience is a strong bonus. - Familiarity with Linux, Git, and high-performance computing environments. Nice-to-Have Areas of Interest - Neural circuit modeling. - Synaptic plasticity and learning. - Large-scale brain simulations. - Data-driven modeling of neural systems. - Interest in wet lab experiments to collect more data or test theories. Education - Ph.D. or equivalent demonstrated expertise in Computational Neuroscience, Neuroscience, Computer Science, Physics, Electrical Engineering, Applied Mathematics, or a related discipline. Location - This position can be remote, hybrid, or onsite at our office in Emeryville, CA. - Some travel may occasionally be required for collaboration and team events. Compensation - The posted wage range is based on location in the Bay Area. - The successful candidate will receive a competitive compensation package, commensurate with their experience and location. Benefits - Cultural Alignment. - Surprising problem-solver – you love finding unexpected solutions to problems we don't even know we have, and see options where others only see constraints. - Flexible by default – you don't draw hard lines around your job description and expect your role to evolve significantly over 6–12 months. - Bias to action – you launch and iterate on thoughtful experiments rather than waiting for perfect plans, and you're energized by novel problems. - High standards, internally driven – you believe you can build the world's best vehicle for unleashing extraordinary individuals to create public goods, and you hold yourself to that bar without benchmarking against others. - Open to feedback – you create opportunities to give and receive honest feedback, staying focused on both personal and team growth, and committing to transparency even when it’s uncomfortable. - Operate with the highest levels of integrity, judgment, and stewardship – you treat residents, peers, and vendors with genuine respect for their time and contributions. - Embrace the tools that define great work today – Astera is building for an AI-driven future and equips the team accordingly. - Understand that theories are opinions until they’re adequately validated with data – follow the scientific method rigorously.