• Develop and optimize machine learning models for parsing, extracting, and categorizing data in PDF tax documents. • Collaborate with cross-functional teams to integrate machine learning solutions into our software products. • Apply statistical analysis to identify signals, trends, and insights that can inform product development. • Mentor and guide junior data scientists and team members, fostering a collaborative and innovative work environment.

• 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.

Fully Remote Position Preferred Locations: Midwest based: Indianapolis, IN or IL, Chicagoland Area preferred Who We Are: K1x is the leading data distribution platform for alternative investments. Simply put, our mission is to digitize the K-1 ecosystem. Our AI-powered K-1 extraction technologies surpass all other competition and we’re the first to produce a digital K-1. Learn more at www.k1x.io About the role:  We are seeking a highly skilled and experienced Machine Learning Engineer to join our dynamic team. The ideal candidate will have a strong background in natural language processing (NLP) and extensive experience working with unstructured and semi-structured data such as financial statements and tax documents. As a Machine Learning Engineer, you will play a critical role in developing and implementing machine learning models that enhance our software’s ability to accurately and efficiently process partnership accounting and tax documents.  If you are an experienced Machine Learning Engineer or Data Scientist looking for an exciting opportunity to work on challenging problems and deliver machine learning products, we would love to hear from you. Join our team and help shape the future of alternative investments management and distribution! Responsibilities - Develop and optimize machine learning models for parsing, extracting, and categorizing data in PDF tax documents.  - Collaborate with cross-functional teams to integrate machine learning solutions into our software products.  - Apply statistical analysis to identify signals, trends, and insights that can inform product development.  - Mentor and guide junior data scientists and team members, fostering a collaborative and innovative work environment.

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