The posting
Role Overview
The Artificial Intelligence Scientist at Precision AI will drive innovation at the intersection of advanced AI research and agricultural applications. This role is responsible for conceiving, researching, and translating novel AI approaches into practical solutions that address complex challenges in agriculture.
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. This role emphasizes problem discovery and ideation, developing novel solutions, and driving research from concept through deployment in collaboration with internal and external partners.
Working closely with AI leadership, engineers, agronomy experts, and strategic partners, the AI Scientist will shape both the long-term technical vision and the day-to-day execution of Precision AI’s AI-powered agricultural solutions.
This hybrid role is based in Calgary and will work from Precision AI’s headquarters 3 days a week.
Key Responsibilities
Research & Innovation Lead applied AI research to develop novel approaches for agricultural challenges such as crop monitoring, yield forecasting, and sustainability. Explore and prototype emerging AI paradigms, including reasoning-enhanced LLMs (e.g., chain-of-thought, self-reflection, tool use), recursive or iterative modeling, reinforcement learning and RLHF-style training, and self-supervised or foundation models. Translate research ideas into validated prototypes and production-ready methods.
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. Advance model robustness, generalization, and efficiency under real-world agricultural constraints.
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.
Scientific Leadership & Mentorship Set standards for scientific experimentation, and reproducibility across AI research efforts. 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. Communicate research findings clearly through technical reports, presentations, and internal knowledge sharing.
Relevant Experience
Required 4+ years of experience in AI/ML model design, training, and deployment in production environments. Proven expertise in building and optimizing models, including LLMs, VLMs, computer vision, and multimodal architecture. Experience with modern learning paradigms such as transfer learning, self-supervised learning, domain generalization, and few-shot or representation learning. Experience with emerging and novel techniques, including retrieval-augmented generation (RAG), diffusion models, reasoning-enhanced LLMs (e.g., chain-of-thought, self-reflection), and reinforcement learning–based training or optimization. Strong programming skills in Python with solid knowledge of data structures, algorithms, and software engineering best practices. Hands-on experience with large-scale data sets, data lake architectures and distributed data processing Fluency in ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) and MLOps practices (CI/CD, experiment tracking, reproducibility). Strong technical communication skills, with the ability to document research, present results, and collaborate effectively across technical and non-technical teams. Proven ability to stay current with AI research, critically evaluate new methods, and apply them to complex real-world problems.
Academic Requirements PhD or master's in computer science, computer engineering, statistics, or mathematics Strong publication record in reputable conferences or journals in AI, machine learning, computer vision, NLP, or related areas
