• Your responsibilities as a technical mentor will be divided into three major tasks. • 1:1 video calls • Virtual Technical Deep-Dive Sessions • Group Q&A sessions • Provide personalized support by addressing learner questions related to course content and projects. • Prior to the call mentors are expected to review the students' previous project submission(s) - provided by the Mentor Success Team when available - and/or any specific areas of the Nanodegree content or project that the student has expressed confusion or difficulty with. • For concept deep-dives, mentors will come prepared with a slide presentation that reviews a Nanodegree concept in more detail, share different use-cases for the concept to broaden student perspectives and understanding, and hold a Q&A session with the learners. • For project walkthroughs, a mentor will come prepared with a slide presentation that outlines each element of the rubric to help learners understand expectations and typical problem areas they may encounter. This can include a mentor “grading” a sample submission to demonstrate what mentors are looking for when reviewing projects. • Host regular sessions (via Slack or video) to address learner queries related to projects and coursework. No prior preparation required.

Role Description We are seeking Chemists (PhD or Master's Degree) with a strong background in upper-undergraduate Chemistry or beyond to design rigorous Chemistry problems used to train and evaluate frontier AI models. This role focuses on creating realistic, exam-quality tasks that are solvable by humans yet challenging enough to expose failure modes in advanced language models. - Design and curate upper-undergraduate and graduate-level Chemistry problems with clear, fully worked solutions. - Create challenging, realistic tasks that expose failure modes in frontier AI systems. - Convert diagram-heavy or multi-part chemistry questions into self-contained, text-only problems with definitive answers. - Write rigorous explanations and verify scientific accuracy throughout the problem and solution. - Evaluate AI-generated responses, diagnose errors, and refine questions to meet difficulty targets. - Review Chemistry tasks created by others and improve clarity, correctness, and realism. - Collaborate with cross-functional teams to maintain consistent standards across large task batches. Commitments Required: 8 hours per day with an overlap of 4 hours with PST. Employment type: Contractor assignment (no medical/paid leave) Duration of contract: 2 months (possible extension) Location: Bangladesh, Brazil, Colombia, Egypt, Ghana, India, Pakistan, Indonesia, Kenya, Nigeria, Turkey, Vietnam Selection process: Home Assignment Qualifications - Master’s or Ph.D., or those currently pursuing a Ph.D./Postdoctoral degree in Chemistry, Chemical Engineering, Biochemistry, or a related field. - Proficiency in at least three of the following tools: GROMACS, LAMMPS, Psi4, PySCF, xtb, crest, Multiwfn, Cantera, CoolProp, NWChem, PyBaMM, RDKit, and Open Babel. - Strong research and analytical skills. Requirements - Proficient in at least three of the following tools: GROMACS, LAMMPS, Psi4, PySCF, xtb, crest, Multiwfn, Cantera, CoolProp, NWChem, PyBaMM, RDKit, and Open Babel.

• Investigate and categorize human‑like cognitive phenomena in language and multimodal AI models • Analyze underlying mechanisms from both computational and cognitive perspectives • Design and run experiments to evaluate model behavior, bias, and reliability • Develop concepts and prototypes to mitigate problematic behaviors in real applications • Contribute to safer, more trustworthy AI for voice assistants and infotainment systems

• Analysis of user behavior both on the functional level (e.g., usage of assistant functions) and on the interaction level (e.g., linguistic patterns, dialog structures, speech behavior) gained through qualitative and quantitative user research, experimental setups, as well as big data campaigns • Derivation of insights and user needs based following human-centered design standards • Conceptualization, design, prototyping and evaluation of system solutions that support users across the product life cycle • Potential research questions are: Learning curves, Usage behavior, Behavioral adaptation, Relationship dynamics.