John Deere
Industrial · Autonomous tractors / See & Spray CV
Currently tracking 6 active AI roles, down 13% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $109k–$170k (avg $151k).
John Deere has 6 active AI-related job listings. The most represented stages are agents and data, each accounting for 33% of the roles. Engineering is the top function, with hiring concentrated in the United States and Brazil. Frequently tagged technologies include agent orchestration, tool use, and RAG.
John Deere currently has 11 active AI-related roles in our index. The most common open titles are: Engineer Perception System Integration (m/f/d), Lead Product Manager, Farmer Insights Applications, Part-Time Student - Data Science & Analytics, Part-Time Student - Data Science and Analytics - Austin, TX or Urbandale, IA, Part-Time Student - Software Engineer - Moline, IL. Most positions are in Engineering and Product.
John Deere's active AI hiring is concentrated in: agents (36%), data (27%), application (18%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
John Deere is hiring AI talent in: United States (8 roles), Brazil (3 roles).
Job postings at John Deere most frequently mention: Robotics, Machine Learning, Software Engineering, Perception, Linux.
In the past 30 days, John Deere has posted 8 new AI-related roles. That is a -27% change versus the prior 30 days (11 → 8).
Industrial · Autonomous tractors / See & Spray CV
| Title | Stage | AI score |
|---|---|---|
| Staff Data Scientist Staff Data Scientist role focused on leading the design, development, validation, deployment, and production support of scalable data science, AI, and machine learning solutions. The role involves building robust analytical methods and production-ready capabilities for various modeling techniques (benchmarking, similarity, causal inference, sequence analysis, impact modeling) using modern data science and cloud platforms. Responsibilities include technical leadership, translating business problems into analytical requirements, developing production-ready models and pipelines, and mentoring other data scientists. | Serve | 7 |