Sessional Lecturer - EDST 2420U Workplace Learning- Spring/Summer 2026 Tracking Code: req1962 Frazer Faculty of Education Number of Sections: 1 Appointment Type: Limited Term Salary Grade: Administered in accordance with Appendix A of the Sessional Lecturers Collective Agreement with PSAC Posting Date: March 25, 2026 Closing Date: April 22, 2026 (7:00 pm EST) Course Name and Number: EDST 2420U - Workplace Learning Location of classes: Synchronous and Asynchronous Online Course Start Date: May 11, 2026 Course End Date: August 28, 2026 Reporting to the Dean and working in close collaboration with the BA EDST Program Director, the Sessional Lecturer provides instruction to undergraduate students in the Frazer Faculty of Education. To view this course description, please click the following link and enter the course code from above: https://calendar.ontariotechu.ca. Responsibilities/Accountabilities: The primary responsibility of the applicant is to (develop and) teach a theoretically, methodologically sophisticated and relevant course at the university level within the parameters of the course description listed above. In keeping with a technology-enhanced learning environment utilizing laptop computers and wireless connectivity, the Sessional Lecturer will deliver the course using a technology-enhanced learning approach that includes a variety of inclusive teaching and learning strategies and assessments. The Sessional Lecturer will prepare, deliver and grade all course material and assignments and be available for consultation with students during and following completion of the course session. This position falls within the bargaining unit represented by the Public Service Alliance of Canada (PSAC) and will be subject to the terms and conditions of the collective agreement between the University and PSAC. The collective agreement may be found on the Human Resources section of our website. Candidates will be required to certify that they are currently legally eligible to work in Canada for the duration of the contract. Qualifications: The successful candidate will have demonstrated excellent teaching contributions with a proven aptitude for teaching at the undergraduate level. Required Skills: - A strong theoretical and practical background in workplace learning and the wide variety of learning that takes place within the workplace, along with the required planning, delivery, and assessment of the learning in a variety of learning environments - Evidence of a well-grounded understanding of curricular and pedagogical design, educational research, and authentic assessment of numeracy and communication skills in either young children or adult learners - A proven ability to meaningfully and effectively integrate technology by designing and leading interactive, inclusive learning experiences in online learning environments, including synchronous, asynchronous, and blended modes - Evidence of flexible support for adult students in online learning, support that maintains academic rigour. - Use of authentic assessment techniques in online environments that foster reflection, creativity, critical thinking, problem-solving, and collaboration in online learning environments - Evidence of an ability to work in a collaborative environment - Excellent communication skills with an ability to maintain effective relationships with a range of faculty, staff and students - Candidates will have demonstrated excellence of at least 2+ years in teaching undergraduate/graduate students in an education program - Demonstrated knowledge and experience with digital learning tools including Canvas, Zoom, and Google Meets - Experience coordinating and supervising Teaching Assistants as applicable Education: - A PhD in Education with a focus on workplace learning OR - A Master’s Degree in education complemented by - Research focus in workplace learning OR - Specialist Qualifications in workplace learning Experience: Preference will be given to those candidates with: - 2+ years experience in proactively advancing equity, diversity, and inclusion in teaching and learning - 2+ years experience teaching online with synchronous and asynchronous components, using a Problem-based learning model Priority will be given to an Internal Applicant who has taught any course for the University in three (3) different sessions in the past thirty-six (36) months, provided they meet the requirements for the position as outlined in the job posting. How to Apply: Interested candidates should submit in electronic format a covering letter and their resume. Applications will be accepted until April 22, 2026 or until a suitable candidate is found. We appreciate all applications received; however, only those candidates selected for an interview will be contacted. Ontario Tech University is actively committed to equity, diversity, inclusion, indigenization and decolonization (EDIID), and welcomes applications from all qualified candidates, while especially encouraging applications from First Nations, Metis, Inuit peoples, Indigenous peoples of North America, racialized persons, persons with disabilities, and those who identify as women and/or 2SLGBTQ+. All qualified candidates are encouraged to apply; however, Canadian citizens, permanent residents, Indigenous Peoples in Canada, and those eligible to work in Canada, will be given priority. Ontario Tech University respects people's different needs and therefore will take all reasonable steps to ensure accommodation for applicants where appropriate. The University is also committed to ensuring that confidentiality is maintained throughout all aspects of the recruitment cycle. If you require accommodation, please contact Julie Day, Health and Disability Management Specialist. For more information about the universities policies for accommodating employees with disabilities please review the university’s Accessibility Policy. The university acknowledges the lands and people of the Mississaugas of Scugog Island First Nation which is covered under the Williams Treaties. We are situated on the Traditional Territory of the Mississaugas, a branch of the greater Anishinaabeg Nation which includes Algonquin, Ojibway, Odawa and Pottawatomi. Job Location: Oshawa, ON, Canada

• Build and fine-tune NLP systems to extract structured data and insights from insurance documents. • Lead the development and fine-tuning of large language models. • Architect training workflows that use user feedback and corrections. • Apply computer vision to extract data from PDFs and unstructured formats. • Build intelligent agents that interact with web interfaces using Playwright or Selenium. • Design robust evaluation pipelines and performance metrics.

Location: Fully Remote  Preferred Locations: Midwest-based; Indianapolis, IN or IL, Chicagoland Area preferred    Who We Are  We are K1X. Our platform powers a modern, all-digital K-1 experience by replacing legacy workflows with scalable software and AI-driven automation.  As we expand our machine learning capabilities, we are investing in a robust ML platform that enables production-grade model development, deployment, and monitoring across our products.  About your Role  We’re seeking an experienced Machine Learning Operations (MLOps) Engineer to join our team and build the infrastructure that powers AI and machine learning at K1X.  This is a hands-on role focused on designing scalable systems, pipelines, and tooling that enable our Machine Learning Engineers to efficiently train, deploy, and operate models in production.  You’ll work at the intersection of software engineering, DevOps, and machine learning—owning the reliability, scalability, and performance of our ML platform.   Your Responsibilities  - Design and build scalable ML infrastructure to support model training, evaluation, and deployment.  - Develop and maintain containerized environments using Docker and Kubernetes.  - Build and manage distributed training pipelines and orchestration workflows.  - Implement and maintain ML lifecycle tooling such as MLflow for experiment tracking and reproducibility.  - Own production inference systems, including NVIDIA Triton Inference Server.  - Design and operate low-latency, high-availability model serving architectures.  - Implement CI/CD pipelines for ML deployment, versioning, and rollback strategies.  - Build and maintain data pipelines integrated with Snowflake and related data systems.  - Implement monitoring, logging, and alerting for model performance, drift detection, and system health.  - Partner with ML Engineers to improve developer experience and accelerate delivery.

Job Title: Senior Software Engineer - Machine Learning & Geospatial Location: 100% Remote (US Based Only) *We cannot sponsor or transfer any visas, of any kind, at this time* Hiring Manager: Senior Engineering Manager Estimated salary range: $165,000 to $190,000 - The salary offered for this position will be based on a candidate’s experience and skill demonstrated during interviews and other evaluations About Ocient: Ocient is a data analytics software solutions company that enables always-on, compute-intensive analysis of complex, large-scale data with outstanding performance that delivers up to 90% price savings. Ocient brings data transformation, loading, complex query processing, AI, OcientML® and OcientGeo® as a single, consolidated solution for deeper insights and data-driven decision making. Enterprises can deploy Ocient’s pilot-to-production solutions on premises, in the OcientCloud® or in the public cloud, with little to no resource-intensive integration. Ocient is a global, carbon-neutral company, headquartered in Chicago, and backed by leading investors including Greycroft, OCA Ventures, In-Q-Tel and Buoyant Ventures. For more information, please visit www.ocient.com. Job Description: We’re looking for a Senior Software Engineer to help evolve our Machine Learning capabilities, with a particular focus on closing feature gaps and behavioral differences relative to widely used ML frameworks (e.g., Spark ML, scikit-learn), while continuing to deliver new ML functionality. This role is ideal for someone who enjoys working across model behavior, system design, and customer expectations — ensuring that ML features behave predictably, perform well at scale, and align with how users expect industry-standard tools to work. Responsibilities: - Design and implement machine learning features used in production customer workflows. - Help identify and close feature and behavior gaps between our ML capabilities and common frameworks (e.g., Spark ML, scikit-learn). - Proactively evaluate semantic differences, defaults, and edge cases that could surprise customers. - Partner with product, architects, and customer-facing teams to anticipate upcoming customer needs and gaps. - Investigate and resolve issues where ML behavior diverges from user expectations (e.g., model output, metrics, configuration semantics). - Contribute to other ML initiatives including new models, metrics, performance improvements, and infrastructure work. - Analyze and improve the performance of existing ML code, balancing correctness and stability with customer facing latency. - Write clear design docs, tests, and documentation to make behavior explicit and prevent regressions. Minimum Qualifications: - 5+ years of experience building production software systems. - Strong proficiency in at least one backend or systems language (e.g., C++, Java, Scala). - Experience implementing or integrating machine learning models in production. - Familiarity with ML libraries or frameworks such as Spark ML, scikit-learn, XGBoost, or similar. - Strong instincts around correctness, edge cases, and behavioral consistency. - Ability to work across teams and codebases to turn ambiguous requirements into concrete solutions. An ideal candidate will have: - Experience comparing or validating behavior across multiple ML frameworks. - Experience with large-scale data systems or analytical databases. - Familiarity with distributed execution, performance tuning, or numerical stability. - Understanding of spherical geometry and its application to geospatial analytics. What success looks like: - Customers see fewer surprises when using ML features compared to familiar frameworks. - ML behavior, defaults, and limitations are well-documented and intentional. - Feature gaps are identified early, not discovered under customer pressure. - You deliver across parity work and broader ML initiatives, balancing short-term needs with long-term quality. We are not open to using an agency or staffing company at this time. We do not accept unsolicited agency or staffing resumes and we are not responsible for any fees related to unsolicited resumes. Ocient provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type and makes all hiring decisions without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. All official Ocient job postings and recruiting communications will come directly from our team via our Careers page, LinkedIn, or from an @ocient.com email address. If you receive communication about a role from any other source, please treat it with caution and direct questions to recruiting@ocient.com