Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation isproject-based, not permanent employment. What this opportunity involves While each project involves unique tasks, contributors may: - Design graduate- and industry-level mechanical engineering problems grounded in real practice. - Evaluate AI-generated solutions for correctness, assumptions, and engineering logic. - Validate analytical or numerical results using Python (NumPy, SciPy, Pandas). - Improve AI reasoning to align with first principles and accepted engineering standards. - Apply structured scoring criteria to assess multi-step problem solving. What we look for This opportunity is a good fit for mechanical engineers with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:  - Degree in Mechanical Engineering or related fields, e.g. Thermodynamics, Fluid Mechanics, Mechanical Design, Computational Mechanics, etc. - 3+ years of professional mechanical engineering experience - Strong written English (C1/C2) - Strong Python proficiency for numerical validation - Stable internet connection  Professional certifications (e.g., PE, CEng, PMP) and experience in international or applied projects are an advantage. How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Payment - Paid contributions, with rates up to $55/hour*  - Fixed project rate or individual rates, depending on the project - Some projects include incentive payments *Note: Rates vary based on expertise, skills assessment, location, project needs, and other factors. Higher rates may be offered to highly specialized experts. Lower rates may apply during onboarding or non-core project phases. Payment details are shared per project.

Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation isproject-based, not permanent employment. What this opportunity involves While each project involves unique tasks, contributors may: - Design graduate- and industry-level mechanical engineering problems grounded in real practice. - Evaluate AI-generated solutions for correctness, assumptions, and engineering logic. - Validate analytical or numerical results using Python (NumPy, SciPy, Pandas). - Improve AI reasoning to align with first principles and accepted engineering standards. - Apply structured scoring criteria to assess multi-step problem solving. What we look for This opportunity is a good fit for mechanical engineers with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:  - Degree in Mechanical Engineering or related fields, e.g. Thermodynamics, Fluid Mechanics, Mechanical Design, Computational Mechanics, etc. - 3+ years of professional mechanical engineering experience - Strong written English (C1/C2) - Strong Python proficiency for numerical validation - Stable internet connection  Professional certifications (e.g., PE, CEng, PMP) and experience in international or applied projects are an advantage. How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Payment - Paid contributions, with rates up to $55/hour*  - Fixed project rate or individual rates, depending on the project - Some projects include incentive payments *Note: Rates vary based on expertise, skills assessment, location, project needs, and other factors. Higher rates may be offered to highly specialized experts. Lower rates may apply during onboarding or non-core project phases. Payment details are shared per project.

• Contribute to cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems • Collect, analyze, and synthesize findings from data and build intelligent data-driven model • Write clean, efficient, and sustainable code • Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across growth, discovery, ads and search • Scope and independently solve moderately complex problems

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description AJU is building a pool of adjunct faculty to design and teach fully asynchronous, online courses to satisfy general education requirements. Courses include: - Life Science (e.g., Introductory Biology for nonmajors; with at home‑/virtual lab) - Physical Science (e.g., Earth Science, Physical Science survey, Chemistry or Physics for nonmajors; with at home‑/virtual lab) - General Mathematics (e.g., Mathematical Ideas, Statistics for Decision Making, College Algebra/Quantitative Reasoning) Faculty will collaborate with AJU’s academic leadership and instructional design team to align outcomes, assessments, and learning activities with Intersegmental General Education Transfer Curriculum (IGETC) standards and best practices for adult online learners. Key Responsibilities - Course Design & Delivery - Develop or refine a fully asynchronous Canvas course using backward design, inclusive pedagogy, and authentic assessments aligned to IGETC outcomes and AJU standards. - Implement online laboratory experiences for science courses (e.g., virtual simulations, remote lab kits, data analysis labs) that meet learning objectives and academic integrity requirements. - Create clear course modules with measurable objectives, short video microlectures, interactive content, and accessible materials (ADA/508 compliant). - Teaching & Student Support - Actively engage adult learners through timely announcements, discussion facilitation, and substantive, rubric guided feedback that motivates student success. - Hold consistent virtual office hours and be available via LMS messaging/email within published response-time standards. - Use data (e.g., LMS analytics, early alerts) to support persistence; submit grades and progress reports on schedule. - Assessment & Continuous Improvement - Align assessments to IGETC and course outcomes; maintain gradebook transparency. - Participate in course reviews, incorporate feedback, and collaborate with program leadership on iterative improvements. - Community & Compliance - Model AJU’s commitment to equity and inclusion; apply culturally responsive teaching practices supportive of diverse, adult, and returning learners. - Adhere to academic policies, student privacy (FERPA), and accessibility standards. Qualifications - Master’s degree in the teaching discipline (Biology, Chemistry, Physics, Earth Science, Mathematics/Statistics, or closely related field), or a master’s degree with at least 18 graduate credits in the discipline taught. - Demonstrated online or adult learner teaching experience (higher education preferred); experience in asynchronous modalities strongly preferred. - For science instructors: experience designing/teaching online labs using virtual simulations. Preferred Qualifications - Experience aligning general education courses to IGETC outcomes (Area 5: Physical and Biological Sciences with lab; Area 2: Mathematical Concepts and Quantitative Reasoning). - Record of excellence in engaging adult and returning learners (frequent, motivating interaction; constructive, rubric based feedback). - Community college or open access institution teaching experience; commitment to equity minded, inclusive pedagogy consistent with sample statements. - Experience with Learning Management Systems (Canvas by Instructure, preferred). - Familiarity with universal design for learning (UDL), OER curation, and accessible course authoring. Workload & Modality - Asynchronous online sections ≈ 16 weeks, with defined weekly cadence. - Standard student contact expectations (announcements, discussion facilitation, feedback within 72 hours, grading turnarounds, and virtual office hours). Compensation & Classification - Part‑time adjunct; compensation equal to $1,666.67 per credit. $5,000 stipend for each course designed. - This position is not benefits eligible. Application Materials - CV/Resume - Cover Letter outlining IGETC relevant teaching experience and online lab/math course design - Unofficial Graduate Transcripts (official required upon hire) - One sample of an online module or syllabus (with an example of an asynchronous lab or math assessment) - List of three references (no letters required at application) Equal Employment Opportunity & Accessibility AJU is an equal opportunity employer. We welcome applications from all qualified individuals and do not discriminate on the basis of race, color, national origin, age, marital status, sex, gender identity or expression, sexual orientation, disability, religion, ancestry, veteran status, or any other protected status. We are committed to providing reasonable workplace accommodations for qualified individuals with disabilities.