Machine Learning Engineer – Edge AI & Embedded Vision | US-Based | Remote | Cutting-Edge Autonomy
Are you passionate about real-time computer vision, model optimization, and deploying AI in the real world? We’re working with a venture-backed startup developing next-generation autonomous systems for defense and industry. They’re on the hunt for an expert
Machine Learning Engineer to lead model deployment on low-cost embedded hardware.
You’ll work at the forefront of embedded AI - training, compressing, and deploying deep learning models that operate efficiently on resource-constrained platforms like Jetson, ARM, and custom ASICs. This is a chance to push the boundaries of what's possible in edge AI.
What you’ll do:
- Design and train deep learning models for real-time computer vision.
- Optimize performance via quantization, pruning, and knowledge distillation.
- Deploy models on platforms like Jetson, Qualcomm, ARM Cortex, and ASICs.
- Contribute to SLAM, object detection, tracking, and sensor fusion pipelines.
- Write robust code in Python and C++, using TensorFlow, PyTorch, and ONNX.
- Collaborate closely across engineering teams to integrate ML into production.
What we’re looking for:
- 5+ years in ML/deep learning with a strong CV/compression focus.
- Hands-on with deployment on embedded hardware or edge devices.
- Fluency in Python, C++, and ML deployment frameworks (TensorRT, ONNX).
- Background in optimizing models for latency, power, and memory footprint.
- Nice-to-have: CUDA/OpenCL, robotics (ROS), SLAM, or multi-sensor fusion.
Why it’s exciting:
- Backed by top-tier defence-focused VCs.
- Working on products that redefine scalable autonomy.
- Competitive salary + full health/dental/vision + 401(k) + flexible PTO.
Due to the nature of the work, U.S. citizenship or Permanent Residency may be required.
Interested in making machine learning move in the real world? Apply now or reach out directly to Ciara Holmes at EVONA for more info
