Top Machine Learning Jobs in Calgary

Develop and optimize auto-labelling systems using ML and computer vision, manage data pipelines, and collaborate with researchers.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

The Director of Engineering will lead and scale Buzz Solutions' engineering organization, focusing on AI, infrastructure, and platform systems while ensuring best practices and high-quality solutions.

The Senior Account Executive will develop relationships with utility clients, lead sales initiatives, negotiate contracts, and maintain sales activities in Hubspot to achieve sales targets.

Lead the architecture, development, and implementation of an enterprise-grade agentic AI orchestration platform. Build reusable AI components and intelligent agents using Claude, LangGraph, RAG, vector databases, MCP, prompt engineering, and multi-agent workflows. Develop scalable FastAPI APIs, ensure AI security and responsible AI practices, support enterprise or regulated data environments, and provide technical leadership across architecture and engineering efforts.

Design, build, and deploy scalable AI/ML systems and pipelines. Develop reusable Python packages, RAG/LLM integrations, agentic workflows, evaluation/monitoring frameworks, optimized data queries, and MLOps practices while collaborating across cross-functional teams.

Bay Area-based hunter Enterprise Account Executive responsible for prospecting, developing, and closing new enterprise business for Anyscale. Manage complex sales cycles, achieve quotas, lead CxO engagements, qualify opportunities, collaborate with marketing, and drive ongoing account growth while advising customers on ML, compute, and cloud solutions.

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

The Applied Scientist will transform raw data into actionable insights by developing machine learning models for personalization, ranking, and optimization to enhance conversion and revenue. This role emphasizes building production systems, frameworks for experimentation, and communication with team members regarding technical challenges.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

Lead the product vision for innovative AI technologies, translating research into marketable products, and collaborating with cross-functional teams.

Seeking a Research Scientist to develop AI models for enhancing 3D reconstruction processes, requiring strong skills in computer vision and neural reconstructions.

The Junior Data Scientist will design and develop data models using Machine Learning and Analytics for trading and compliance in finance.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

Develop scalable web applications using React and Next.js, implement backend solutions in Python, maintain DevOps practices, and collaborate with AI/ML teams to deploy applications.

The role involves designing and implementing machine learning systems for knowledge representation and temporal reasoning, focusing on organizational intelligence. Responsibilities include building LLM-powered pipelines, developing memory algorithms, and researching various ML techniques.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

Lead R&D for underwater acoustics: define scientific vision, develop detection/classification/localization and acoustic mapping capabilities, guide sensing design and deployments, run field experiments and analyses, translate research into operational products, and represent the company to partners and customers.

Lead development of Sofars underwater acoustics platform: design detection, classification, localization, and acoustic mapping methods; run field experiments and simulations; translate research into operational products; collaborate across engineering and science teams; represent Sofar externally.

Lead Vegetation Intelligence product, owning roadmap from ML detection models to customer-facing risk products. Advance detection models, integrate outputs into outage and ignition risk, run user research, and translate model capability into actionable B2B products used in the field.

Design, build, and operate a cloud-native data and ML platform handling billions of events. Own end-to-end pipelines, feature stores, model training/serving, observability, and data product SLAs. Apply distributed systems, MLOps, and LLM/agentic systems to ensure reliable, low-latency access to features and production models with business-focused impact.

As a Research Intern, you will prototype and productionize research in AI and cryptographic schemes, collaborating with teams for real-world impact.

Build full-stack marketplace features and ML Ops infrastructure for search, ranking, and renter-facing products. Develop model deployment pipelines, feature stores, real-time data pipelines, and monitoring using Chalk and Vertex AI. Collaborate with data scientists to productionize models, improve system performance and reliability, write tested code, participate in reviews, plan technical work, and communicate effectively across distributed teams.

Build full-stack marketplace features for search, ranking, and renter-facing products while supporting ML Ops infrastructure. Develop model deployment pipelines, feature stores, real-time data pipelines, monitoring, and production integrations with data scientists. Write tested code, review contributions, plan technical work, and improve system performance, reliability, and scalability. Collaborate with product and globally distributed engineering teams.

Own the end-to-end machine learning lifecycle, including dataset creation, research, rapid experimentation, model development, deployment, monitoring, and optimization. Build production-grade ML and LLM solutions, apply techniques such as quantization and pruning, track experiments systematically, and maintain scalable MLOps pipelines. The role requires strong Python and PyTorch expertise, production ML experience, and hands-on knowledge of LLM fine-tuning, prompt engineering, RAG, and efficient inference.