Remote ML Engineer - Autonomous Agriculture & Perception

Ladders; United States; On-site; USD 113,000 - 202,000; Full time

Benefits offered by this job

Visa sponsorship

Remote US

Annual bonus

Diversity & Inclusion

Career development

Job summary

Ladders is seeking a Machine Learning Engineer Autonomy to join a leader in Enterprise Technology. Remote work is available for US-based candidates, with compensation ranging from $113,000 to $202,000 per year, reflecting experience and impact.

You will design CVML models for perception tasks, own the ML lifecycle, and collaborate with cross-functional teams to deploy autonomous agriculture solutions. The role emphasizes scalable delivery and architecture influence.

Qualifications

• Bachelor's or Master's Degree in Computer Science or related field.
• 2 years of experience developing high-performance ML systems for segmentation, depth estimation, and related perception tasks.
• Proficiency in Python and PyTorch/TensorFlow.
• Solid understanding of vision and deep learning fundamentals.
• Excellent communication skills for interdisciplinary collaboration.

Responsibilities

• Design and optimize CVML models and training pipelines for perception tasks.
• Own the full ML development lifecycle, from prototyping to production deployment.
• Collaborate with engineering teams to integrate models into the autonomous tractor initiative.
• Analyze and improve model performance through rigorous testing and data evaluation.
• Apply machine learning techniques to advance autonomous agriculture solutions.

Skills

Communication

Collaboration

Problem solving

Education

Bachelor's or Master's Degree in Computer Science or related field

Tools

Python

PyTorch

TensorFlow

Job description

Ladders is seeking a Machine Learning Engineer Autonomy to join a leader in Enterprise Technology. Remote work is available for US-based candidates, with compensation ranging from $113,000 to $202,000 per year, reflecting experience and impact.

You will design CVML models for perception tasks, own the ML lifecycle, and collaborate with cross-functional teams to deploy autonomous agriculture solutions. The role emphasizes scalable delivery and architecture influence.