Role Description The University of Fredericton is seeking multiple Subject Matter Experts to support the continued development of upper-year courses in UFred's fully online Bachelor of Computer Science program. These are short-term contract opportunities focused on the initial academic development of Year 3 and Year 4 courses. The work will primarily involve: - Syllabus development - Course and program outcome alignment - Assessment planning - Creation of representative sample assessments Courses under consideration span a broad range of core and elective areas, including: - Artificial intelligence and machine learning - Cybersecurity - Enterprise application development - Theoretical computer science - Related upper-year computing fields Specific course assignments will be discussed with the selected contractor(s) based on their academic background, teaching experience, and areas of expertise. Qualifications - A completed doctorate in Computer Science or a closely related discipline - Demonstrated academic, research, teaching, or professional competence in the subject area of the course under consideration - Experience developing or substantially revising post-secondary courses, curricula, assessments, or instructional materials - Experience writing measurable learning outcomes and aligning course content and assessments with those outcomes - Strong written communication and organizational skills - The ability to develop academically rigorous material for undergraduate learners - The ability to work within established program, accreditation, and course-development requirements - The ability to meet agreed-upon development and revision timelines - Experience developing courses for asynchronous online delivery is considered an asset Requirements These are fixed-fee independent contractor engagements. Compensation will be established in advance for the completion of the defined deliverables and will not be based on hourly billing. The agreed fee, scope, milestones, and deadlines will be set out in the individual contract. Each initial contract will normally focus on: - The syllabus - Course structure - Assessment plan - Representative sample assessments for a single course The anticipated development period is approximately two to four weeks. During this period, the Subject Matter Expert will work with the Program Chair or designated full-time Computer Science faculty to align the course with program expectations. Once the Program Chair is satisfied with the academic deliverables, approximately one additional week will be allowed for UFred's internal team to review the materials for alignment with institutional processes. Benefits - Consideration for additional syllabus and sample-assessment contracts - First consideration for a later contract to complete the full development of the course - Opportunity to teach the course after full development, subject to program needs, qualifications, performance, and availability Application Process Qualified contractor(s) are invited to submit: - A current curriculum vitae - A brief statement identifying their areas of subject-matter expertise - A description of their post-secondary course or curriculum-development experience - An indication of their availability over the coming months - The technical areas or types of courses they would be most interested in developing Successful contractor(s) whose experience aligns with current course needs will be invited to meet with the Program Chair to discuss suitable course assignments, contract scope, timelines, and fit.

• Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) • Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. • Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX • Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. • Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. • Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team.

Role Description We are looking for an experienced and pragmatic Senior Computer Vision Engineer to take ownership of key components of our production computer vision pipeline. This is a hands-on role for an engineer who thrives on solving complex challenges and is passionate about building robust, reliable systems. - Maintain, optimize, and ensure the reliability of our complex, multi-stage computer vision pipeline, which includes models for semantic segmentation, object detection, image classification, and optical flow estimation. - Set the technical vision and roadmap for specific parts of the CV pipeline, making key architectural and algorithmic decisions to drive performance and accuracy. - Lead the adaptation of existing models and the development of new components for upcoming projects, translating business needs into technical solutions. - Oversee the entire data lifecycle for your models, from defining clear annotation guidelines to managing data quality and iterating on the feedback loop with our in-house annotation team. - Write clean, maintainable, and robust production-level Python code, adhering to strong software engineering best practices. - Manage model training, experimentation, and evaluation on our on-premise Linux server infrastructure. Qualifications - Master's degree or PhD in Computer Science, or a related field. - 5+ years of professional experience building and deploying computer vision models in a production environment. - Deep, hands-on expertise with PyTorch. - Proven experience across a range of computer vision tasks (e.g., object detection, semantic segmentation, image classification). - Exceptional software engineering fundamentals with a focus on writing clean, testable, and efficient Python code. - High proficiency in a Linux environment is mandatory. You should be comfortable and efficient using shell scripting, SSH, and command-line interfaces for daily work. - Demonstrated experience working with data annotation pipelines, defining guidelines, and ensuring data quality. - Excellent problem-solving skills and the ability to work independently and take ownership of projects. - Willingness to embrace new ideas and feedback, work collaboratively with others, and continuously seek improvement. Requirements - Experience working with large-scale datasets (TBs) and building efficient data processing pipelines. - Experience with model optimization techniques (e.g., quantization, pruning, TensorRT/ONNX conversion). - Experience mentoring junior engineers or providing technical leadership. Benefits - Colleagues who strive for excellence while using the latest technologies. - Educational budget for your personal growth. - Above-average corporate pension plan. - Work from home up to 5 days a week. - Join more than 90 different nationalities in a truly international and diverse working environment. Company Description AUTO1 Group is Europe's leading digital automotive platform. Within AUTO1, our dedicated Computer Vision team is at the forefront of innovation, building the next generation of technology for the used car market. Our mission is to create a new, objective standard for vehicle assessment. We develop and deploy proprietary devices that automatically inspect used cars for exterior damages like scratches, dents, and other imperfections, bringing unprecedented transparency and efficiency to the industry. We are a focused, engineering-driven team building complex, real-world systems that merge hardware, software, and deep learning.

• Design, train, and iterate on custom object detection models specifically tuned for retail environments, inventory tracking, and product recognition • Fine-tune and deploy open-source vision-language models (LLaVA, Qwen-VL, InternVL, PaliGemma, etc.) for product understanding, zero-shot classification, and scene reasoning • Build vision-language-action pipelines that translate visual understanding into downstream decisions • Take state-of-the-art models and optimize them for edge deployment through quantization, pruning, and architectural optimization • Build robust data pipelines and annotation workflows to continuously improve model performance on diverse retail scenarios • Stay ahead of the curve on CV and VLM research, prototype new architectures, and determine what's production-ready • Mentor engineers, establish best practices for model development, and drive technical decisions around our CV infrastructure