Machine Learning Engineer

• Build and maintain workflows and infrastructure supporting the end-to-end ML lifecycle
• Partner with researchers and ML practitioners to productionize models and enable faster iteration
• Design, build, and improve data and training pipelines
• Improve data processing, annotation workflows, and ML system efficiency
• Deploy and maintain background systems supporting model training and inference
• Build tooling and processes for monitoring model performance, system reliability, and operational health
• Improve scalability, observability, and reproducibility of ML systems
• Optimize ML infrastructure for speed, reliability, and cost-efficiency
• Identify bottlenecks and automate or streamline manual ML workflow processes
• Establish best practices around ML operations, deployment, and system performance

• Design and own a cloud-native big data platform handling audience data for millions of attendees and billions of interactions annually
• Design and own ML infrastructure including feature stores, training pipelines, model serving, and monitoring
• Ensure reliable, low-latency access to features and ML infrastructure
• Own the full data pipeline from change data capture through validation, transformation, and denormalization
• Connect data-system performance and reliability to customer and business outcomes
• Build data products with defined SLAs, strong data health, and discoverability
• Use AI coding agents such as Claude Code
• Build LLM-powered pipelines and autonomous agents to enrich, classify, and act on audience data at scale
• Collaborate with product and engineering teams and communicate technical decisions to stakeholders

Senior Software Engineer, Machine Learning

• Build closed-loop decision systems using machine learning to operate consumer businesses more intelligently
• Develop systems for causal response estimation, uncertainty quantification, action selection, information generation, outcome observation, policy updates, challenger evaluation, and guarded deployment
• Work across causal and heterogeneous treatment-effect modeling, uncertainty estimation and calibration, contextual bandits, active learning, sequential decision-making, policy learning, constrained optimization, counterfactual and off-policy evaluation, experimentation, champion/challenger systems, and production ML infrastructure
• Produce measurable economic lift in controlled experiments
• Build systems that generalize across businesses, learn from interventions, and safely automate commercial decisions
• Own problems end to end, from framing and modeling through production deployment and evaluation
• Report to the CTO
• Participate in recruiter screening, virtual interview rounds, an in-person interview at the Costa Rica office, and reference checks

Senior Machine Learning Engineer, Causal & Decision Systems

• Design, train, and optimize deep neural networks for image-based classification, regression, detection, and re-identification
• Develop and optimize architectures for real-time inference on resource-constrained platforms
• Analyze requirements and evaluate machine learning approaches based on performance, computational, and hardware constraints
• Participate in the full development lifecycle, from data acquisition and annotation through model training, evaluation, optimization, and integration
• Develop and improve tools for model training, retraining, testing, and deployment
• Evaluate existing technologies and components, recommend new approaches, and integrate relevant solutions
• Design and test proofs of concept and prototypes
• Collaborate within a multidisciplinary team of computer vision, machine learning, and software development specialists
• Contribute to the evolution of Genetec’s on-device analytics solutions

Machine Learning Specialist – Device Analytics

• Build and maintain core infrastructure powering Quora's machine learning platform
• Ensure high availability, scalability, and performance of ML platform infrastructure
• Build and improve distributed systems serving production ML models, including LRMs and LLMs
• Work on GPU model serving and optimize latency, throughput, and cost
• Contribute to PyTorch-first standardization and ML ecosystem modernization
• Build tooling that improves ML engineers' development, testing, and deployment velocity
• Modernize the feature store to accelerate productionization of new features
• Participate in the on-call rotation and help resolve production issues
• Ship production work within the first few weeks while learning from senior and staff engineers

Software Engineer, New Grad – Machine Learning Platform

• Build conversational AI systems for phone and SMS that understand customer needs, take action, and determine when to act autonomously
• Develop memory, knowledge graphs, and validated customization tooling for dealership-focused agents
• Train, evaluate, and deploy ML models using Ray Serve and Dagster for prediction, classification, ranking, and capacity forecasting
• Maintain ML models in production
• Create offline and online evaluations, simulations, and CI gates to measure quality and catch regressions
• Implement shared evaluation, observability, and LLM tooling
• Contribute to model serving, LLM infrastructure, and production observability
• Raise engineering standards through design and code reviews, technical writing, and mentorship
• Work autonomously and help create clarity in ambiguous situations
• Ship AI features including prompts, agents, tools, and production ML models that interact with real customers

Senior Machine Learning Engineer

• Develop and deploy machine learning models for camera-based perception in autonomous trucks, including object detection, segmentation, depth estimation, and scene understanding
• Own end-to-end model development from data curation and training through evaluation and deployment
• Write production-quality ML code for scalable training, evaluation, and inference pipelines
• Analyze model performance across diverse driving scenarios, identify failure modes, and improve robustness and generalization
• Improve large-scale training pipelines, including dataset preparation, distributed training, and experiment tracking
• Partner with data teams on dataset quality, labeling strategies, and edge-case coverage
• Collaborate with perception, simulation, and validation teams to integrate models into the autonomy stack
• Improve tooling, workflows, and infrastructure for experimentation and model iteration
• Contribute to model architecture decisions and technical discussions
• Mentor junior engineers on implementation, debugging, and best practices

Senior Machine Learning Engineer – Camera Model

• Develop, deploy, and maintain ML models for customer segmentation, acquisition, pricing, and lifetime value use cases
• Translate business problems into analytical frameworks
• Collaborate with cross-functional teams to define success metrics and solution approaches
• Evaluate model performance, fairness, and stability through model evaluation, A/B testing, and production monitoring
• Communicate complex technical results to non-technical stakeholders and provide actionable insights
• Manage relationships across business lines, corporate and/or control functions
• Ensure alignment with enterprise and regulatory requirements
• Keep current on emerging trends and developments
• Grow knowledge of the business, related tools, and techniques
• Act as a brand ambassador for the business area/function and TD
• Help evaluate and build Agentic AI, Generative AI, and traditional ML solutions using Pro Code or Low Code tools
• Potentially manage people

Applied Machine Learning Scientist II

• Design, code, and deploy machine learning models and generative AI use cases
• Enhance machine learning use-case development, model training, and feature-engineering pipelines
• Develop new generative AI use cases and enhance existing solutions
• Partner with the Data Analytics team to translate business requirements into technical solutions
• Provide technical influence by sharing knowledge, mentoring team members, and establishing best practices
• Ensure code quality, best practices, and scalability across deliverables
• Research emerging trends and industry best practices and evaluate implications for business strategy

• Own and execute research and development of pCTR and conversion prediction models
• Improve calibration, reduce training data biases, and advance model accuracy across Instacart ads surfaces
• Design and implement debiasing techniques including Mixed Negative Sampling, Inverse Propensity Weighting, counterfactual risk minimization, Platt scaling, and isotonic regression
• Contribute to Multi-Domain Multi-Task model architecture using Mixture-of-Experts, Transformer layers, and LoRA adapters
• Contribute to sequence modeling initiatives including TIGER generative retrieval and Semantic ID representation learning
• Collaborate on Foundation Models using autoregressive user behavior prediction
• Formulate ambiguous modeling problems and translate business observations into ML research directions with clear evaluation criteria
• Publish and present findings internally
• Contribute through design reviews, paper sharing, and experiment retrospectives

Machine Learning Engineer jobs in Canada