Precision AI

www.precision.ai

2 Jobs

40 Employees

About the Company

At Precision AI we are on a mission to accelerate artificial intelligence based farming practices to create healthier, happier, and more profitable farms. By leveraging our advanced drones and custom-built AI technology, we can take crop production decisions from a whole field to an individual plant level. This type of decision-making transforms an industry that has been reliant on larger and broader technology for decades. The outcome of our solutions is integrated into the agricultural technology of today and helps craft the machines of tomorrow that will feed the world. Precision AI was founded in 2017 with offices in Canada and the United States. We are scaling rapidly with an elite global team solving the agriculture challenges of farms around the world.

Listed Jobs

Company Name: Precision AI
Job Title: Artificial Intelligence Scientist
Job Description: **Job title** Artificial Intelligence Scientist **Role Summary** Lead applied AI research for agricultural technology, translating cutting‑edge research into production models for crop monitoring, yield forecasting, and sustainability solutions. Shape technical vision and ensure model robustness in real‑world farming environments. **Expectations** - 4+ years of AI/ML engineering in production. - Deep expertise in LLMs, VLMs, computer vision, and multimodal systems. - Proficient in Python, PyTorch/TensorFlow, Hugging Face, and MLOps pipelines. - Strong academic background (PhD or MSc) with peer‑reviewed publications. - Excellent communication for cross‑functional collaboration and knowledge sharing. **Key Responsibilities** 1. **Research & Innovation** – Conduct applied research on novel AI techniques (e.g., reasoning‑enhanced LLMs, RLHF, self‑supervised learning) for agricultural challenges. 2. **Model Development** – Design, train, evaluate, and deploy state‑of‑the‑art models across CV, NLP, time‑series, and multimodal domains using satellite/drone imagery, sensor data, and textual agronomy inputs. 3. **Domain Integration** – Incorporate agronomy, climate, and geospatial knowledge to handle noisy, sparse, seasonal, and region‑specific data. 4. **Scientific Leadership** – Set experimental standards, ensure reproducibility, mentor engineers/scientists, and lead research methodology discussions. 5. **Collaboration** – Work with AI leaders, agronomy experts, and external partners; produce technical reports, presentations, and internal documentation. **Required Skills** - Advanced ML/AI research and model architecture design. - Expertise with modern learning paradigms: transfer learning, domain adaptation, few‑shot learning, representation learning, spatiotemporal modeling, multimodal fusion. - Proficiency in programming (Python), data structures, algorithms, and software engineering best practices. - Experience with large‑scale data lakes, distributed processing, and MLOps (CI/CD, experiment tracking). - Strong technical writing and presentation skills. **Required Education & Certifications** - PhD or Master’s in Computer Science, Computer Engineering, Statistics, Mathematics, or related field. - Published research in reputable AI/ML/Computer Vision/NLP conferences or journals.

Company Name: Precision AI
Job Title: Artificial Intelligence Engineer
Job Description: Job Title: Artificial Intelligence Engineer Role Summary: Design, build, train, and deploy AI‑driven models for agricultural applications, advancing precision spraying systems through advanced machine learning techniques. Expectations: 2+ years of production AI/ML experience, strong proficiency in Python, modern deep learning frameworks, and MLOps practices. Must demonstrate expertise in LLMs, VLMs, diffusion and multimodal models, and be comfortable applying state‑of‑the‑art techniques such as transfer learning, prompt engineering, retrieval‑augmented generation, and efficient inference. Key Responsibilities: - Lead AI/ML projects from conception to production, establishing milestones and ensuring timely delivery. - Build, train, evaluate, and optimize machine learning models across NLP, computer vision, and multimodal domains, including LLMs, VLMs, CNNs, ViTs, and diffusion models. - Apply advanced methods: transfer learning, parameter‑efficient fine‑tuning, prompt engineering, knowledge distillation, multimodal fusion, quantization, pruning, and model compression. - Implement and maintain robust code architecture using Python, data structures, algorithms, OOP, and design patterns. - Write unit/integration tests, set up CI/CD pipelines, manage version control (Git), and document code following team standards. - Containerize and orchestrate models with Docker and Kubernetes; design and expose APIs (REST/GraphQL) for integration. - Manage scalable cloud infrastructure on AWS, including large‑scale datalake architectures and distributed data processing. - Monitor, troubleshoot, and maintain deployed models and services. - Mentor junior engineers, conduct code reviews, workshops, and documentation. - Communicate progress, challenges, and results clearly across technical and non‑technical stakeholders. Required Skills: - Python programming (≥2 years), data structures, algorithms, OOP. - Deep learning frameworks: PyTorch, TensorFlow, Hugging Face. - MLOps: CI/CD, experiment tracking, reproducibility. - Building training pipelines for LLMs, VLMs, diffusion, and multimodal models. - Fine‑tuning techniques, prompt engineering, RAG, info‑efficient inference. - Cloud services (AWS), Docker, Kubernetes, REST/GraphQL APIs. - Distributed data processing, datalake management. - Strong communication, documentation, and presentation skills. Required Education & Certifications: - Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Statistics, or Mathematics.