• Build and maintain large-scale distributed training and evaluation pipelines for Deep Reinforcement Learning. • Design scalable infrastructure for training, evaluation, model management, and experiment tracking. • Build dashboards and monitoring tools to track training progress, model quality, compute usage, and agent performance. • Optimize the training and inference performance of our Deep Learning models. • Improve cost efficiency across cloud/GPU infrastructure and make high-impact infrastructure decisions. • Work closely with researchers and engineers to reduce iteration time and improve model accuracy. • Help design reproducible ML workflows, including data pipelines, checkpointing, evaluation, versioning, and deployment. • Identify bottlenecks across the full ML stack: model architecture, data loading, GPU utilization, distributed training, inference, and infrastructure. • Contribute directly to ML improvements that increase accuracy, robustness, and compute efficiency.

Role Description We are looking for engineers with passion for using machine learning to create intelligent applications. As a Machine Learning Engineer, you will be part of the Data Science team inside the Product and Tech group with +2200 brilliant developers, engineers, analysts and researchers. You will collaboratively design, build, and productionize machine learning systems. As a member of the logistics data science team, you will develop solutions to supply and demand challenges while optimizing logistics algorithms to ensure a reliable, efficient, and profitable service. From predicting ETAs to solving pricing problems, our ML pipelines form the backbone of our delivery operations. Location: Remote Canada Reporting to: Technology Manager, Machine Learning - Proven academic/industry experience in Machine Learning or similar, developing technologies for forecast or logistics products. - Strong coding and software engineering skills in a mainstream programming language; Python preferred. - Able to work independently and solve complex problems, with a demonstrated ability to productize and deploy using cloud services (eg. Amazon Web Services, Google Cloud Platform). - Strong background in working with microservices and event-driven architecture, serverless computing, and cloud architecture patterns. - Familiarity with ML tools and packages like TensorFlow, pyTorch etc. - Practical experience building and optimizing production quality machine learning applications making use of engineering best practices, automation and experimentation. - Understanding of machine learning algorithms and ability to apply them in data driven processing systems. - Ability to quickly prototype ideas/solutions, perform critical analysis, and use creative approaches for solving complex problems. - Ability to collaborate closely with multi-functional teams, driving forward best practices. - Clear oral and written communication. Qualifications - Researching, implementing, and deploying innovative ML techniques applicable to logistics problems. - Scaling and transferring novel machine learning solutions to improve our decision making processes and solutions in their different product use cases. - Implementing and launching Data Science applications that coordinate with front facing solutions; constant improvements using performant and efficient ML models. - Implementing tools, data pipelines, and evaluation frameworks to enable development, iteration, and launch of ML ideas within the product scope. Benefits - Our teams forge connections internally and work with some of the best-known brands on the planet, giving us truly international impact in a dynamic environment. - Fun, fast-paced and supportive, the JET culture is about movement, growth and about celebrating every aspect of our JETers. - Inclusion, Diversity & Belonging: We’re committed to creating an inclusive culture, encouraging diversity of people and thinking, in which all employees feel they truly belong and can bring their most colourful selves to work every day. Company Description Want to know more about our JETers, culture or company? Have a look at our career site where you can find people's stories, blogs, podcasts and more JET morsels.

• Define end-to-end architecture for AI/ML solutions • Serve as a strategic advisor to clients • Architect scalable solutions using cloud-native AI tools • Lead integration of Generative AI into enterprise applications • Design retrieval-augmented generation (RAG) systems • Guide teams on MLOps frameworks • Evaluate build-vs-buy decisions for AI tools • Evaluate emerging technologies in AI space • Mentor technical teams and strengthen organizational expertise • Collaborate with leadership to align strategies with business objectives • Represent the organization at industry events • Drive cross-functional collaboration to identify opportunities

• Define end-to-end architecture for AI/ML and Gen AI, Agentic AI, MCP systems including data pipelines, model training/inference, and MLOps • Serve as a strategic advisor / consultant to clients, leading solution design discussions • Architect scalable solutions using cloud-native AI tools (Azure ML, AWS SageMaker, or GCP Vertex AI) • Lead the integration of Generative AI into components / features leveraging LLMs into enterprise applications using APIs • Design retrieval-augmented generation (RAG) systems with vector databases • Guide teams on MLOps frameworks for CI/CD, model versioning, monitoring, and automated retraining • Evaluate emerging technologies and trends in AI, ML, Gen AI, Agentic AI space • Mentor technical teams and guide solution architects, AI/ML engineers • Ensure ethical and responsible AI practices