Senior/Staff Machine Learning Engineer, Perception

🏢 Agtonomy · 📍 South San Francisco, CA · 💼 hybrid · ⏰ Full Time 💰 $200,000 - $280,000 / yr
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Position Summary & Responsibilities

About Us

At Agtonomy, we’re not just building tech—we’re transforming how vital industries get work done. Our Physical AI and fleet services turn heavy machinery into intelligent, autonomous systems that tackle the toughest challenges in agriculture, turf, and beyond. Partnering with industry-leading equipment manufacturers, we’re creating a future where labor shortages, environmental strain, and inefficiencies are relics of the past. Our team is a tight-knit group of bold thinkers—engineers, innovators, and industry experts—who thrive on turning audacious ideas into reality. If you want to shape the future of industries that matter, this is your shot.

About the Role

We're looking for a skilled ML engineer to build the perception systems that give our autonomous machines human-like awareness in rugged, unstructured environments. You'll develop computer vision and machine learning systems that turns noisy camera and LiDAR data into robust 3D scene understanding — enabling heavy equipment to operate safely through dust, glare, occlusion, and whatever messy conditions a working site throws at it.

The field is moving past bounding-box detection and hand-tuned tracking toward learned, dense scene representations, foundation-model-driven data engines, and uncertainty-aware perception. You'll be at the center of that shift. This role is hands-on: you'll write production-grade software, distill and optimize models for embedded hardware, and validate your work on real machines at operating around the world.

🎯 Candidate Preparation Guide For Agtonomy Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

Evaluates cutting-edge robotics, AI, and autonomous vehicle software applied specifically to heavy machinery and agricultural equipment.

📋 Preparation Priorities (Key Areas to Clarify):
  • 1. Designing complex robotics solutions that fail to account for dust, mud, vibration, and GPS-denied farm environments.
  • 2. Being overly academic with zero appreciation for rapid, iterative field testing.
  • 3. Missing core safety-critical mindset requirements for autonomous heavy machinery.
✨ Recommended Role Alignment Hook:

I build robust autonomy stacks that solve real-world perception and control challenges for off-highway machinery.

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