Role Description - Design challenging computational problems using domain-specific scientific software libraries like libRoadRunner, Tellurium, or SBML-based tools. - Develop problems that test AI systems' ability to implement complex multi-step scientific workflows and strategic reasoning. - Calibrate tasks against state-of-the-art AI models, refining problem design to achieve target difficulty. - Collaborate with AI research teams to improve model outputs and training data quality. - Work independently and asynchronously to meet deadlines and improve AI model performance. Qualifications - Graduate-level training in a relevant STEM domain (MS, PhD, or equivalent research experience). - Proficiency with at least one listed scientific software library, evidenced by research publications, open-source contributions, or professional work. - Strong Python programming skills for writing problem setups, oracle functions, and solution validators. - Ability to work independently and iterate on problem designs based on calibration feedback. - Comfortable in a Linux/terminal environment with remote compute sandboxes. Requirements - Experience across multiple listed domains or tools. - Familiarity with benchmark or evaluation design. - Background in scientific pedagogy or exam/problem-set design. - Experience with computational reproducibility and containerized environments. Benefits - Compensation: $70–$100/hour - Location: Remote - Commitment: 15–20 hours/week Application Process - Upload resume - AI interview based on your resume - Submit form Resources & Support - For details about the interview process and platform information, please check: Interview Process - For any help or support, reach out to: support@mercor.com - PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.

• Train and fine-tune language models powering our AI companions • Own and improve agent harnesses, agentic loops, and the chat interface algorithm • Build and maintain the full LLM stack — from model training to production deployment • Track cutting-edge NLP research and open-source developments; translate them into an NLP roadmap • Collaborate closely with the validation, content, and dataset preparation teams to design experiments and measure model quality

Role Description As a PhD Research Intern, you will apply your deep technical expertise to solve complex, real-world problems. You are a highly motivated researcher with a passion for applying cutting-edge AI, Machine Learning, and software engineering to create tangible business value. This is a project-based internship. You will be paired with a dedicated mentor and given a high-impact project to own, from research and design through to a functional prototype; you'll: - Collaborate with senior engineers, product managers, and domain experts to understand our customers' needs and challenges. - Apply your research skills to design, code, and test novel AI/ML solutions. - Develop, evaluate, and iterate on AI/ML models (including LLMs) to solve a core business problem. - Analyze existing platforms to identify bottlenecks and propose opportunities for algorithmic or systems-level improvements. - Gain hands-on experience with our production systems, including CI/CD pipelines (GitHub Actions) and Infrastructure as Code (Terraform). - Present your findings and project outcomes to the team and key stakeholders at the end of your internship. Qualifications - Currently enrolled in a PhD program in Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a related quantitative field. - Demonstrated research experience in AI, Machine Learning, Deep Learning, or Generative AI (e.g., publications, conference papers, or significant project work). - Strong programming skills in at least one of our core languages: Python, Java, C#, or GoLang. - Deep understanding of modern ML algorithms, platforms, and packages (including deep learning and LLMs). - Solid grasp of probability and statistics concepts (e.g., distributions, Bayesian inference, maximum likelihood estimation). - A collaborative mindset and strong communication skills, with an ability to translate complex research into practical applications. - Evidence of innovative problem-solving and a drive to learn and excel. Requirements - Experience with cloud platforms (e.g., AWS). - Familiarity with software development best practices (SDLC), CI/CD tools, and IaC (e.g., Terraform). - Familiarity with container orchestration (e.g., Kubernetes). - Experience or interest in the financial technology (FinTech) industry. Benefits - Health, dental and vision insurances. - Retirement savings plan. - Paid time off. - Health savings account. - Flexible spending accounts. - Life insurance. - Disability insurance. - Tuition reimbursement. - And more. Pay Range $35.00 - $43.00

Role Description Computational Thinking across Disciplines is an introduction to computational thinking as a way of thinking across a variety of disciplines. The course is designed to develop and enhance problem-solving skills and computer coding abilities at a variety of levels, while addressing issues that are directly of interest to the learner and individual areas of study. The course is offered asynchronously in a fully online format. As an online course, the content and syllabus has already been created for the course. Qualifications - Master’s degree in Education - Scholarship and/or leadership in computational thinking - Post-secondary teaching experience in computational thinking - Teaching experience in an online environment - Bachelor of Education and/or K – 12 teaching certification - Teaching experience at the postsecondary level in an education program Requirements - Salary: $10,212.40 - Application Deadline: June 20, 2026 - Submit with Application: - CV (required) - Candidate Application Form (CAF) - Names and Contact Information for Referees - Evidence of Good Teaching (e.g., teaching philosophy; assessment practices; scholarship that informs your teaching; evaluations) - Verification of highest degree - Optional: Cover Letter, Teaching Dossier, Sample Course Outline Benefits - This appointment is in accordance with the Contract Teaching Faculty and Part-time Librarians Collective Agreement. - All applicants are assessed using both the “Appendix H: Assessment of CTF Candidates under 13.6.1” in the collective agreement and the program specific rubric. Company Description Wilfrid Laurier University endeavors to fill positions with qualified candidates who have a combination of education, experience, skills, and abilities to successfully perform the duties of the position while demonstrating Laurier's Employee Success Factors. Diversity and creating a culture of inclusion is a key pillar of Wilfrid Laurier University’s Strategic Academic Plan and is one of Laurier’s core values. - Laurier is committed to increasing the diversity of faculty and staff and welcomes applications from qualified members of the equity-deserving groups. - Indigenous candidates who would like to learn more about equity and inclusive programming at Laurier are welcome to contact the Office of Indigenous Initiatives. - Candidates from other equity-deserving groups who would like to learn more about equity and inclusive programming at Laurier are welcome to contact Equity and Accessibility.