• Advance research on agentic AI systems trained on real-world interaction signals and multimodal data. • Design and experiment with learning paradigms for large-scale models, including RAG, supervised fine-tuning, RLHF, DPO, and GRPO-style methods. • Develop multimodal representation learning approaches, including joint embedding spaces across text, audio, logs, and structured interaction traces. • Improve speech and audio intelligence capabilities, including STT, ASR, and audio-driven learning signals. • Research methods for enhancing agent reasoning, planning, tool use, and adaptation in real-world environments. • Define how complex behavioral and interaction signals can be translated into effective training objectives for large-scale models. • Build and refine evaluation methodologies for agent performance in real-world, domain-specific scenarios. • Collaborate with engineering and product teams to bring research ideas into production systems. • Identify patterns in real-world workflows and convert them into generalizable modeling and representation strategies. • Contribute to the long-term research direction of Toptal’s agentic AI systems and multimodal capabilities. • Stay current with academic and industry research and integrate relevant advancements into internal systems.

• Review AI-generated outputs related to meditation, wellbeing, mental health, sleep, anxiety, stress, and behavior change. • Score and label AI outputs using structured rubrics and evaluation frameworks. • Assess whether outputs are clear, safe, evidence-informed, clinically appropriate, and useful for real-world users. • Check claims against available evidence, including academic literature, clinical guidance, and internal research standards. • Identify recommendations that are unsafe, overstated, unsupported, vague, misleading, or poorly explained. • Flag potential issues related to indications, contraindications, evidence quality, risk, and real-world applicability. • Compare AI outputs across different prompts, workflows, models, or evaluation conditions. • Help prepare short research summaries, evidence notes, and internal review documents. • Support prompt testing and AI evaluation workflows, including documenting what works, what fails, and why. • Use tools such as ChatGPT, Claude, and other AI systems to support research, synthesis, review, and day-to-day work. • Communicate structured feedback to the Head of AI, Clinical AI Research Lead, product team, and engineers. • Help improve the quality, safety, and usefulness of AI-powered wellbeing experiences over time. • Support internal research and publication-related work where relevant, under supervision.

Role Description We are looking for experienced Machine Learning and AI Experts to help train and evaluate advanced AI models. In this role, you will apply your domain expertise to assess AI-generated content, create high-quality technical prompts and answers, and evaluate model responses for accuracy, relevance, and clarity. This is a remote, flexible opportunity for experts who want to contribute to the development of better AI systems. Compensation: US$150 per hour Key Responsibilities - Assessing the factual accuracy, technical quality, and relevance of AI-generated text related to Machine Learning and Artificial Intelligence. - Crafting and answering advanced questions across Machine Learning, AI, and adjacent technical domains. - Evaluating, comparing, and ranking AI-generated responses based on correctness, reasoning quality, completeness, and clarity. - Identifying errors, inconsistencies, hallucinations, or weak reasoning in model outputs. - Providing clear feedback that helps improve the performance of AI systems. Qualifications - A PhD in Machine Learning, Artificial Intelligence, or an adjacent field, such as Computer Science, Statistics, Applied Mathematics, Computational Neuroscience, Natural Language Processing, Computer Vision, Reinforcement Learning, or Data Science. - Experience working as a Machine Learning Engineer, AI Researcher, Applied Scientist, Data Scientist, or in a comparable highly technical or analytical role. - Strong understanding of core Machine Learning and AI concepts, including model training, evaluation, optimization, and real-world application. - Ability to write clearly, precisely, and fluently in English about complex technical topics. - Strong analytical judgment and attention to detail. Work Arrangement - Remote - Flexible hours

• 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.