Precision Ai
Artificial Intelligence Scientist
Calgary, CAN
Sponsorship not specifiedDetected 95 days ago
PythonData StructuresAlgorithmsCI/CDMachine LearningTensorFlowPyTorchNLPComputer VisionLLMsRAGMLOpsStatisticsA/B TestingForecastingResearchExperimental DesignLeadershipCommunicationCollaborationMentoringPublic SpeakingAgronomy
About the role
- This role is responsible for conceiving, researching, and translating novel AI approaches into practical solutions that address complex challenges in agriculture.
- This role is hybrid working out of our Calgary office.
Responsibilities
- Research & Innovation Lead applied AI research to develop novel approaches for agricultural challenges such as crop monitoring, yield forecasting, and sustainability.
- Agricultural Intelligence Integration Integrate domain knowledge from agronomy, climate, and geospatial data into model design and evaluation.
- Develop methods that handle noisy, sparse, seasonal, and region-dependent data, common in agricultural systems.
- Mentor engineers and scientists on research methodology, model design, and experimental analysis.
- Collaboration & Knowledge Sharing Collaborate with cross-functional teams and external research partners to align research outcomes with real-world impact.
- Role Overview The Artificial Intelligence Scientist at Precision AI will drive innovation at the intersection of advanced AI research and agricultural applications.
- This role emphasizes problem discovery and ideation, developing novel solutions, and driving research from concept through deployment in collaboration with internal and external partners.
Requirements
- Relevant Experience 4+ years of experience in AI/ML model design, training, and deployment in production environments.
Benefits
- The AI Scientist will lead the scientific direction of AI initiatives by designing and overseeing research-driven AI projects and developing and advancing state-of-the-art machine learning models.
- Advanced Model Development Design and evaluate state-of-the-art models across computer vision, NLP, time-series, and multimodal learning (e.g., satellite/drone imagery, sensor data, text).
- Apply modern techniques such as representation learning, domain adaptation, few-shot learning, multimodal fusion, spatiotemporal modeling, and efficient fine-tuning.
- Proven expertise in building and optimizing models, including LLMs, VLMs, computer vision, and multimodal architecture.
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