• Develop and adapt Deep Learning-based methods for NuRec world reconstruction, novel view synthesis, generative image and video modeling, scene completion, and neural rendering • Apply your extensive knowledge of Deep Learning tools and methods, 3D Computer Graphics, Computer Vision, and simulation • Prototype experimental solutions with Python, PyTorch, modern DL frameworks, and AI tools for fast RnD iteration • Improve the realism, consistency, and controllability of generated scenes, especially for viewpoints and trajectories beyond the originally captured sensor path • Build evaluation and agentic AI-assisted research workflows for quality triage, regression detection, experiment automation, and faster model iteration • Work with a team of researchers and developers to turn promising research results into robust, usable technology

• Develop novel ML models and algorithms for computational perception and interaction • Design and execute rapid experiments to validate new ideas and methods • Explore advancements in computer vision, audio processing, sensor fusion, or related domains • Collaborate with ML Engineers to integrate research outcomes into training pipelines and production systems • Measure and track experimentation velocity, the number and quality of validated experiments per quarter • Contribute to research planning and help define the technical roadmap alongside the Engineering Manager and Tech Lead • Document research findings and communicate technical progress to the broader team

• 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.