AI, Sensor Fusion & Precision Phenotyping Intern

Vylor; Indianapolis (IN); On-site; USD 45,000 - 67,000; Full time

Job summary

Vylor in Indianapolis is seeking an AI, Sensor Fusion & Precision Phenotyping Intern for a 3-month Summer 2027 research internship. You will join the AI & Breeding group to develop field-deployed perception systems that turn raw sensor data into quantitative plant measurements.

Work spans computer vision, 3D geometry, LiDAR and multi-modal sensor fusion on ground robotic platforms in real breeding environments, with ownership from problem framing through algorithm development, field validation,

Qualifications

• Pursuing MS/PhD in CS/CE/Robotics or related field; PhD preferred.
• GPA 3.0+ preferred.
• Strong foundation in CV, ML, robotics or phenotyping, with 3D geometry knowledge.
• Proficient Python with PyTorch/TensorFlow.
• Software engineering: clean APIs, modular code, Git, testing.
• Hands-on LiDAR, 3D reconstruction, camera/LiDAR calibration, or multi-sensor fusion.
• Strong analytical and communication skills.

Responsibilities

• Extract quantitative plant traits from multi-modal field imagery—RGB, LiDAR, multispectral, hyperspectral, thermal, and IMU/GNSS—using 3D computational geometry and sensor-fusion techniques.
• Implement and execute repeatable, well-documented workflows that apply established computer vision and machine learning methods with sensor-fusion-driven pre- and post-processing, delivered as modular components and scalable pipelines.
• Own a scientific research project with direct plant breeding impact: design experiments, quantify accuracy and failure modes, collaborate with breeders, geneticists, and engineers, and communicate results to technical and business audiences.

Skills

Computer Vision

Machine Learning

Robotics

Python programming

Git

3D point cloud processing

Sensor fusion

Education

MS/PhD in CS/CE/Robotics or related

Tools

Open3D

PCL

ROS/ROS 2

COLMAP

OpenCV

PyTorch3D

CloudCompare

Helios

Job description

Join Vylor—Powering the Future of Agriculture. At Vylor, we’re advancing agriculture through breakthrough science—combining elite germplasm, cutting-edge biotech, and next-generation expertise in gene editing and molecular breeding. Our portfolio includes trusted industry brands like Pioneer®, Brevant®, and Hoegemeyer®, delivering performance farmers rely on. Join us to help scale innovation globally and shape what’s next in crop science. Vylor is seeking an AI, Sensor Fusion & Precision Phenotyping Intern for a 3-month research internship starting Summer 2027. You will join our Artificial Intelligence & Breeding group and help develop field-deployed perception systems that turn raw sensor data into quantitative plant measurements at scale. The work spans computer vision, 3D computational geometry, LiDAR and multi-modal sensor fusion, and machine learning, applied to data collected by ground robotic platforms in real breeding environments. Expect meaningful ownership of an R&D project from problem framing through algorithm development, field validation, and technical communication.

Key Responsibilities

• Extract quantitative plant traits from multi-modal field imagery—RGB, LiDAR, multispectral, hyperspectral, thermal, and IMU/GNSS—using 3D computational geometry and sensor-fusion techniques.
• Implement and execute repeatable, well-documented workflows that apply established computer vision and machine learning methods with sensor-fusion-driven pre- and post-processing, delivered as modular components and scalable pipelines.
• Own a scientific research project with direct plant breeding impact: design experiments, quantify accuracy and failure modes, collaborate with breeders, geneticists, and engineers, and communicate results to technical and business audiences.

Qualifications

• Currently pursuing an MS or PhD in Computer Science, Computer Engineering, Robotics, Electrical Engineering, Agricultural/Biosystems Engineering, or a related field; PhD candidates preferred.
• Enrolled at an accredited university during the internship; minimum 3.0 GPA preferred.
• Solid foundation in computer vision, machine learning, robotics, or computational phenotyping, with working knowledge of 3D computational geometry (point cloud processing, coordinate transforms, calibration and registration, and geometric feature analysis).
• Strong Python programming and hands‑on experience with modern machine learning frameworks (PyTorch, TensorFlow).
• Software engineering fundamentals: clean API and framework design, modular code, version control (Git), and disciplined testing.
• Hands‑on experience with LiDAR point cloud processing, 3D reconstruction, camera/LiDAR calibration, or multi‑sensor fusion.
• Strong analytical, experimental, and problem‑solving skills.

Preferred Qualifications

• Experience building perception systems for outdoor, unstructured environments (agriculture, autonomous vehicles, field robotics, construction, or mining).
• Background in plant breeding, computational plant phenotyping, crop science, or agricultural robotics.
• Experience integrating multispectral, hyperspectral, or thermal imaging with RGB and/or LiDAR data.
• Familiarity with Open3D, PCL, ROS/ROS 2, COLMAP, OpenCV, PyTorch3D, CloudCompare, Helios, or similar 3D and perception toolchains.
• Experience with AWS or MLOps for large‑scale machine learning pipelines, or with sensor calibration and synthetic data / simulation (Isaac Sim, Gazebo, Blender, Unreal).
• Research publications, open‑source contributions, patents, or demonstrated real‑world technical projects.

What We Value

We are looking for someone who is self‑motivated, technically curious, hands‑on, and accountable—comfortable operating independently in an ambiguous R&D environment, quickly learning new technologies and problem domains, and taking ownership of delivering meaningful technical outcomes.

Are you a good match? We seek applicants from all backgrounds to ensure we get the best, most creative talent on our team.

Corteva announced to separate our current advanced seed and genetics business to establish a standalone company, operating as Vylor, Inc. The process of the planned separation is anticipated to be completed the fourth quarter of 2026, subject to legal requirements and board approval. Should you accept this position, it is anticipated that, following conclusion of the separation, you would be an employee of Vylor, Inc. and your employment would be governed by Vylor, Inc. employment processes, programs, policies, and benefit plans. In that case, details of any planned changes would be provided to you by Vylor, Inc. at an appropriate time and subject to any necessary consultation processes.

Vylor is an equal opportunity employer. We are committed to embracing our differences to enrich lives, advance innovation, and boost company performance. Qualified applicants will be considered without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, military or veteran status, pregnancy related conditions (including pregnancy, childbirth, or related medical conditions), disability or any other protected status in accordance with federal, state, or local laws.