Machine Learning Engineer
US$200,000 – US$280,000 / yr base salary
Posted 9 Jul 2026
Advertised as “Senior/Staff Machine Learning Engineer, Perception”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 6+ yrs
- Management level
- Individual contributor
- Employment
- Hybrid
- Contract
- Full-time
| Base salary | US$200,000 – US$280,000 / yr |
|---|
Field of studyComputer Science (or related)
Study levelMaster's or higher
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.
What You'll Do
- Develop real-time perception models for open-world obstacle and terrain understanding.
- Build multi-modal fusion that combines camera and LiDAR into a unified 3D/BEV representation, robust to occlusions, sensor degradation, and GNSS outages.
- Optimize models for low-latency inference on resource-constrained hardware, balancing accuracy and performance.
- Design auto-labeling pipelines that leverage foundation models and teacher-student distillation to scale labeling and close the loop from real-world field interventions.
- Design data and evaluation pipelines that curate large multi-sensor datasets and surface failures fast, with strong visualization and debugging tooling.
- Analyze performance metrics and iterate on algorithms to improve accuracy and efficiency of various perception subsystems.
What You'll Bring
- A MS/PhD in Computer Science, AI, or a related field, or 6+ years of industry experience building vision-based perception systems.
- Deep expertise developing and deploying modern perception models: detection, segmentation, mono/stereo/metric depth, BEV/occupancy, sensor fusion, and 3D scene understanding.