• Develop and apply AI, muon tomography, seismic imaging, and geophysical inversion methods to discover and characterize copper, nickel, lithium, cobalt, and rare earth deposits. • Develop stochastic and ensemble inversion frameworks that assimilate muon flux, seismic, magnetics, gravity, and EM data into 3D subsurface property models. • Forward modeling, sensor placement optimization, and inversion of cosmic-ray muon attenuation data. • Apply deep generative models, geostatistical priors, and physics-informed neural networks for resource estimation. • Extend Mineral-X’s intelligent agent framework for data acquisition decisions.

• Collaborate with a cross-functional group of technologists, product managers, and archivists to shape the future of ethical AI in journalism. • Use generative AI to surface archival content for contemporary use. • Partner with RAD to design and prototype workflows that transform unstructured archival data into structured, discoverable assets. • Establish standards for the ethical use of artificial intelligence with NPR's archival content. • Prepare digital collections for machine learning and research approaches that pair AI with human editorial judgment and metadata to find new insights from the archive. • Identify opportunities and build functional prototypes that enable reporters and producers to query historical audio for contemporary reporting. • Assist in the development of automated workflows to transform unstructured legacy media into a searchable, AI-indexed library. • Analyze historical data troves to surface evergreen content opportunities and extract narrative leads that support contemporary reporting. • Evaluate AI outputs and discovery tools for accuracy and bias to ensure technical prototypes maintain archival integrity.

• Review and evaluate AI-generated outputs related to meditation, wellbeing, mental health, sleep, anxiety, stress, and behavior change. • Assess whether AI outputs are clinically appropriate, evidence-informed, safe, clear, and useful for real-world users. • Evaluate the strength and quality of evidence behind wellbeing, meditation, and psychotherapeutic techniques, including limitations and contraindications. • Help define frameworks for assessing indications, contraindications, risk, evidence quality, internal and external validity, and real-world applicability. • Work with engineers and product teams to improve prompts, AI workflows, evaluations, and user-facing wellbeing experiences. • Translate clinical, psychological, and research concepts into clear product guidance that engineers and designers can act on. • Help build internal evaluation systems for AI outputs, including rubrics, review processes, and quality standards. • Contribute to the development of safe, high-quality AI experiences that are grounded in meditation, wellbeing, and mental health expertise. • Support the creation of structured content and intervention pathways across areas such as sleep, anxiety, stress, mindfulness, and emotional wellbeing. • Help identify where AI outputs are unsupported, overstated, unsafe, unclear, or misaligned with best available evidence. • Collaborate with internal and external researchers on studies, evidence reviews, and potential peer-reviewed publications. • Help shape Insight Timer’s broader research agenda around meditation, wellbeing, mental health, AI, and consumer health products.

• Design, build, and iterate on agentic AI systems for complex healthcare workflows, including documentation, coding, denial management, appeals, and revenue cycle automation. • Develop long-horizon agent behavior across context construction, retrieval, tool use, memory, routing, verification, escalation, and human-in-the-loop review. • Define what “good” looks like for clinical agents end-to-end, translating expert workflows into specifications, rubrics, gold standards, test cases, and clinically meaningful success criteria. • Build rigorous evaluation and feedback loops using expert review, production logs, model outputs, and benchmarks to measure performance, regressions, edge cases, safety, reliability, provenance quality, and business impact. • Prototype new AI capabilities from 0 → 1, then harden them into reliable, explainable, auditable production systems with clear contracts, monitoring, evidence, rationale, and performance gates. • Partner with research and ML engineering teams on model selection, fine-tuning, reward modeling, distillation, synthetic data, post-training, and internal AI infrastructure, including instrumentation, experiment tracking, benchmarking, prompt/version management, and reproducible evaluation.