John Deere
Industrial · Autonomous tractors / See & Spray CV
Industrial · Autonomous tractors / See & Spray CV
John Deere currently has 11 active AI-related job listings. The roles are primarily focused on agents, accounting for 36% of the openings, followed by data roles at 27%. Engineering is the dominant function, with 10 of the listings. The company is hiring for these positions in the United States and Brazil. Frequent technology tags include model serving, agent orchestration, and RAG, suggesting a focus on deploying and managing AI agents. In the last 30 days, John Deere posted 8 new AI roles, representing a 27% decrease compared to the previous 30-day period.
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 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).
| Title | Stage | AI score |
|---|---|---|
| Senior AI Engineer - Indaiatuba/SP Senior AI Engineer role focused on building and operating backend APIs and workflows that expose AI accelerator capabilities to product teams. The role involves end-to-end delivery of AI capabilities, applying responsible AI/LLMOps practices, and working with RAG/agentic workflows. Requires strong Python and full-stack experience, cloud familiarity (AWS), and CI/CD practices. | AgentServe | 7 |