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Ai In Agriculture Jobs (NOW HIRING)

Syngenta

AI Engineer Java

Fallston, NC · On-site

Company Description Syngenta Crop Protection is a leader in agricultural innovation, bringing ... We are looking for an AI Engineer - Java to enhance the Java layer of our internal developer ...

Syngenta

AI Engineer Python

Fallston, NC · On-site

Company Description Syngenta Crop Protection is a leader in agricultural innovation, bringing ... We are looking for an AI Engineer - Python to enhance the Python layer of our internal developer ...

Burro

Senior Robotics AI Engineer

Philadelphia, PA · On-site

$99K - $137K/yr

Burro is the leading robotics company focused on alleviating labor shortages in agriculture through innovative robotic solutions. They are seeking a Senior Robotics AI Engineer to design, deploy, and ...

Syngenta

Global Creative Operations Lead

Fallston, NC · Hybrid

Company Description Syngenta Group , a global leader in agricultural technology and innovation ... The role brings together creative direction, operational leadership, AI-enabled ways of working ...

Produce Careers Inc.

Sales Consultant

Oxnard, CA · On-site

We are looking for someone who understands how decisions are actually made in agriculture, someone ... Educate potential customers on the company's AI-powered harvesting solutions. * Work closely with ...

Tamus

Postdoctoral Research Associate

Dallas, TX · On-site

... Agriculture (CEA) Engineering Lab as a Postdoctoral Research Associate. The candidate will primarily work in one or more of the following areas: computer vision, AI-driven automation, and/or robotics ...

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Ai In Agriculture information

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$11K

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$117.5K

How much do ai in agriculture jobs pay per year?

As of Aug 23, 2026, the average yearly pay for ai in agriculture in the United States is $69,500.00, according to ZipRecruiter salary data. Most workers in this role earn between $34,000.00 and $105,000.00 per year, depending on experience, location, and employer.

What is AI in agriculture?

AI in agriculture refers to the use of artificial intelligence technologies, such as machine learning, computer vision, and robotics, to improve farming practices and productivity. These technologies can help with tasks like crop monitoring, pest detection, yield prediction, and automated machinery operation. By analyzing large amounts of data, AI solutions enable farmers to make more informed decisions, optimize resources, and reduce environmental impact. This leads to increased efficiency and sustainability in agricultural operations.

How does an AI in agriculture professional typically collaborate with agronomists and farm managers on technology implementation projects?

AI in Agriculture professionals often work closely with agronomists and farm managers to understand real-world challenges and develop solutions tailored to specific crop, soil, and climate needs. Collaboration includes gathering field data, explaining AI model outputs, and training staff on new systems. Regular communication and feedback loops ensure that AI tools are both practical and user-friendly, leading to more successful technology adoption on farms. This teamwork approach helps bridge the gap between cutting-edge technology and daily agricultural operations.

What are the key skills and qualifications needed to thrive as an AI in agriculture specialist, and why are they important?

To thrive as an AI in Agriculture Specialist, you need a strong background in agriculture, data science, and programming, often supported by a degree in agricultural engineering, computer science, or a related field. Experience with machine learning frameworks (like TensorFlow or PyTorch), geographic information systems (GIS), and agricultural data platforms is typically required. Strong analytical thinking, problem-solving abilities, and effective communication are crucial soft skills for this role. These skills are vital for developing and implementing AI-driven solutions that optimize crop yields, resource use, and overall farm productivity.

What is the difference between Ai In Agriculture vs Agricultural Data Analyst?

AspectAi In AgricultureAgricultural Data Analyst
Required CredentialsDegree in Agriculture, Computer Science, or related fields; knowledge of AI and machine learningDegree in Agriculture, Data Science, Statistics, or related fields; proficiency in data analysis tools
Work EnvironmentFieldwork, research labs, tech companies, agricultural firmsOffice settings, research institutions, agricultural companies
Industry UsageDeveloping AI solutions for crop management, pest detection, yield predictionAnalyzing agricultural data to inform decisions, optimize processes

Ai In Agriculture focuses on developing and implementing AI technologies in farming, while Agricultural Data Analysts interpret data to support agricultural decisions. Both roles require a strong background in agriculture and data skills, but Ai In Agriculture emphasizes AI development, whereas Data Analysts focus on data interpretation and reporting.

How can AI be used in agriculture?

AI in agriculture involves using machine learning algorithms and data analysis to optimize crop management, monitor plant health, and predict yields. Agricultural professionals may work with sensors, drones, and satellite imagery to gather data and develop solutions that improve efficiency and sustainability.

Which AI tool is best for agriculture?

AI in agriculture involves tools like precision farming software, crop monitoring systems, and drone-based imaging platforms such as John Deere's See & Spray or Climate FieldView. These tools help optimize yields, monitor crop health, and automate tasks, requiring knowledge of data analysis and sensor integration for effective use.
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Ai In Agriculture jobs near you
Infographic showing various Ai In Agriculture job openings in the United States as of August 2026, with employment types broken down into 82% Full Time, and 18% Part Time. Highlights an 94% In-person, and 6% Remote job distribution, with an average salary of $69,500 per year, or $33.4 per hour.

AI Engineer Java

Syngenta

Fallston, NC • On-site

Full-time

Posted 9 days ago

Job description

Company Description

Syngenta Crop Protection is a leader in agricultural innovation, bringing breakthrough technologies and solutions that enable farmers to grow productively and sustainably. We offer a leading portfolio of crop protection solutions for plant and soil health, as well as digital solutions that transform the decision-making capabilities of farmers. Our 17,900 employees serve to advance agriculture in more than 90 countries around the world. Syngenta Crop Protection is headquartered in Basel, Switzerland, and is part of the Syngenta Group. 

Our employees reflect the diversity of our customers, the markets where we operate and the communities which we serve. No matter what your position, you will have a vital role in safely feeding the world and taking care of our planet. Join us and help shape the future of agriculture.

Job Description

We are looking for an AI Engineer - Java to enhance the Java layer of our internal developer platform: templates, shared libraries, runtime standards and quality tooling. This is an opportunity to work at the intersection of advanced technology and agricultural R&D, partnering with engineers, product leaders and scientific stakeholders on capabilities with real-world impact. Help accelerate the digital capabilities that support more sustainable agriculture and scientific innovation.

What You'll Do

  • Lead and apply deep expertise in Modern Java 17+, Spring Boot and Maven/Gradle to deliver durable, scalable outcomes.
  • Lead and apply deep expertise in Shared libraries and service templates to deliver durable, scalable outcomes.
  • Lead and apply deep expertise in AI-assisted engineering with rigorous evaluation to deliver durable, scalable outcomes.
  • Lead and apply deep expertise in GitLab CI/CD, Docker, Kubernetes and Terraform to deliver durable, scalable outcomes.
  • Lead and apply deep expertise in Test engineering: JUnit, Mockito and contract testing to deliver durable, scalable outcomes.
  • Lead and apply deep expertise in Documentation-as-code and agent-consumable interfaces to deliver durable, scalable outcomes.
  • Design and architect scalable solutions that integrate AI capabilities with existing Java-based systems and frameworks.
  • Optimize application performance, monitor system health, and implement observability practices across the platform.
  • Conduct thorough code reviews, mentor junior engineers, and establish best practices for AI-assisted development workflows.
  • Collaborate across engineering, product and scientific teams to translate complex needs into practical solutions.
  • Set a high bar for quality, documentation and thoughtful use of AI-assisted engineering.
Qualifications

What You Bring

  • Demonstrable experience in Modern Java 17+, Spring Boot and Maven/Gradle.
  • Demonstrable experience in Shared libraries and service templates.
  • Demonstrable experience in AI-assisted engineering with rigorous evaluation.
  • Demonstrable experience in GitLab CI/CD, Docker, Kubernetes and Terraform.
  • Demonstrable experience in Test engineering: JUnit, Mockito and contract testing.
  • Demonstrable experience in Documentation-as-code and agent-consumable interfaces.
  • Demonstrable experience in Internal platform thinking.
  • Proven expertise in designing and architecting scalable, distributed systems.
  • Strong background in application performance optimization and observability practices.
  • Experience conducting code reviews, mentoring engineers, and establishing best practices.
  • Strong communication skills and the ability to influence across technical and non-technical stakeholders.
  • Fluent English, written and spoken.

Bonus Points

  • Experience in agriculture, life sciences, scientific computing or a regulated enterprise environment.
  • Experience building platforms, products or services used by multiple internal teams.
  • Experience working in distributed, international engineering organisations.
  • Track record of establishing quality standards and best practices in AI-assisted development workflows.
Additional Information

Why Syngenta?

Meaningful impact: help build technology that supports scientific innovation and sustainable agriculture.

Complex, modern challenges: work on high-scale platforms and systems with room for technical judgement.

Collaborative environment: partner with global engineering, R&D and product communities.

Growth: broaden your influence through challenging work, visible outcomes and a global network.

Apply