Top Machine Learning Jobs in Calgary

Design, build, and deploy scalable ML platforms and pipelines; optimize and monitor model performance; enforce model governance and data compliance; mentor junior engineers; integrate AI solutions with product and engineering teams to deliver measurable business outcomes.

Design and build scalable .NET backend services and gRPC microservices; optimize data layers (SQL Server, MongoDB, Elasticsearch); implement cloud integrations on AWS and Azure; ship AI/ML features (LLMs, RAG, vector search, ranking, personalization) and operate MCP servers; contribute to CI/CD, observability, containerization, and production ML integrations impacting high-traffic consumer marketplaces.

Design, build, and scale MLOps infrastructure and CI/CD/continuous training pipelines. Deploy and monitor ML models as microservices or batch jobs, manage cloud GPU/CPU resources with IaC, enable observability for model drift and latency, and collaborate with data scientists, engineers, and product teams to ensure reproducibility, compliance, and production reliability.

The role involves building production ML models for voice AI, focusing on fine-tuning, deploying, and evaluating models to enhance human-like interactions.

As a Sales Executive, you will hunt for new clients, manage enterprise accounts, and create revenue streams while collaborating with internal teams.

The role involves building standardized training frameworks, profiling model performance, collaborating with teams, and improving related tooling.

The role involves designing and implementing MLOps pipelines, optimizing machine learning processes, and ensuring model performance and compliance in a collaborative environment.

The Senior AI/ML Engineer will develop AI systems for enterprise decision-making, focusing on LLMs and real-time data processing. Responsibilities include building detection systems, insight engines, evaluation frameworks, and collaborating with engineers to ensure robust AI product engineering.

Design and implement machine learning algorithms for robotic manipulation, focusing on RL and IL. Collaborate across disciplines to ensure integration and successful deployment.

Lead design and deployment of production-grade AI solutions, build multi-agent systems and cloud-native ML infrastructure, apply rigorous statistical evaluation, implement generative and predictive models, champion MLOps and data pipeline best practices, and collaborate with clients to translate business needs into technical solutions.

The AI/ML Engineer will design and deploy advanced AI models, optimize performance, and directly engage with government officials to transform industries through innovative AI solutions.

Develop AI-powered features, design APIs, integrate AI/ML models, and enhance developer experience for ClickHouse's platform.

As a Machine Learning Ops Engineer, you'll develop robust code, build and maintain MLOps infrastructure, QA systems, and mentor junior engineers to enhance AI model performance for mining clients.

As a Forward Deployed ML Engineer, you will apply machine learning to mining operations, develop neural network architectures, and communicate findings to non-technical stakeholders while working independently.

The engineer will develop algorithms and optimizations for NVIDIA's LPX inference stack, focusing on compiler components, performance metrics, and collaboration with hardware teams.

Own end-to-end ML projects focused on recommendation and LTV signals: develop, evaluate, and deploy models; build training/scoring pipelines; run A/B tests; monitor production models; collaborate with product and analytics; mentor colleagues.

The role involves building scalable ML models, mentoring engineers, translating business requirements into specs, and ensuring solutions meet performance constraints.

Lead development of scalable molecular dynamics pipelines, integrating physics-based models with machine learning frameworks to enhance molecular engineering and experimentations.

The ML Research Scientist will design Bayesian optimization frameworks, collaborate with lab scientists, and integrate ML models with experimental data.

The role involves designing de novo frameworks for molecular engineering, collaborating across teams to optimize data-driven biological experiments and integrating ML models with experimental systems.

Develop algorithms and optimizations for LPX inference and compiler stack, focusing on deep learning and performance enhancements across NVIDIA systems.

The role involves developing algorithms for inference optimization, building compiler components, and collaborating across teams to enhance performance on NVIDIA platforms.

Design, build, and deploy scalable ML solutions using GCP and Vertex AI. Collaborate with teams to operationalize models and monitor performance.

Seeking Machine Learning Systems Engineers to build end-to-end 3D native ML systems, focusing on training and inference challenges.

Responsible for designing experiments to evaluate AI models, developing evaluation methodologies, analyzing performance data, and collaborating on production insights.