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# Perception Machine Learning Engineer

- **Company:** Saildrone, Inc.
- **Location:** Alameda, CA, United States
- **Salary:** $144,500.0 - $322,742.0
- **Contract:** Permanent contract
- **Skills:** Computer Vision, C++ (Programming Language), Nvidia CUDA, Python (Programming Language), Machine Learning, Sensor Fusion, Software Deployment, Pytorch, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT
- **Published:** November 5, 2026
- **Apply:** https://www.careerbuilder.com/job-details/perception-machine-learning-engineer-alameda-ca--b5d57b2f-6e56-4d67-a5e3-82e9fa3e3f7f

## About the Role

Machine Learning, Systems Administration/Management, Aerospace and Defense, Sales Operations, Alternative Energy, Hubs, Manufacturing, Business Development, Research & Development (R&D), Machine Learning, * Strong Python programming skills.

 * Hands-on experience developing computer vision or machine learning systems.
 * Strong experience with PyTorch or a comparable ML framework.
 * Strong understanding of computer vision fundamentals and real-world visual data.
 * Experience evaluating models using quantitative metrics and experimental analysis.
 * Experience taking ML or computer vision systems into production.

NICE TO HAVE

 * C++ experience for production perception systems.
 * Robotics, autonomous vehicle, aerospace, or defense experience.
 * Edge or embedded ML deployment.
 * Sensor fusion, 3D perception, or multimodal perception.
 * ONNX, TensorRT, CUDA, or other inference optimization experience.

## Description

Saildrone is applying modern machine learning and computer vision to autonomous vehicles operating in challenging, poorly observed environments. As a Perception Machine Learning Engineer, you will develop and deploy ML-based perception capabilities using real-world vehicle data.

You will work across model development, data, evaluation, and production deployment.

THE RESPONSIBILITIES

 * Develop machine-learning-based perception systems for autonomous vehicles.
 * Train, evaluate, and improve computer vision models using real-world sensor data.
 * Develop data and experimentation workflows to identify failure modes and improve model performance.
 * Build perception capabilities for detection, classification, segmentation, depth estimation, or scene understanding.
 * Analyze vehicle data and model failures to identify weaknesses and drive improvements.
 * Integrate models into production perception systems.
 * Bring new machine learning techniques from experimentation into deployed systems.