Tycho AI
Machine Learning Engineer - Computer Vision & Robotics
About this role
Tycho.AI seeks a Machine Learning Engineer to design and deploy efficient computer vision and robotics models for autonomous systems in GPS-denied environments. You'll optimize ML models for edge devices, build training pipelines, and work with state-of-the-art autonomy and perception techniques at a fast-growing MIT-spun defense tech company.
What you'll do
- Design and optimize ML models for computer vision and robotics under constrained compute and memory budgets
- Build and maintain large-scale training and fine-tuning pipelines
- Deploy and compress models using quantization, distillation, and acceleration for embedded accelerators
- Profile and tune on-device inference for latency, throughput, and accuracy trade-offs
- Develop clean, efficient code in Python and/or C++
- Research and implement state-of-the-art techniques in edge autonomy and perception
What they're looking for
- Computer vision and robotics ML applications
- PyTorch or TensorFlow
- Python and C++
- Model deployment and training pipeline development
- Model optimization (quantization, pruning, distillation)
- ONNX, TensorRT, or OpenVINO
- ROS2 or robotics middleware
- SLAM, 3D perception, or sensor fusion
Benefits
- Competitive salary and meaningful equity
- Generous benefits package
- High-trust, collaborative culture
- Work on cutting-edge autonomous systems
- Opportunity to shape company culture and ecosystem
- Rapid scaling startup environment
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Tycho AI
Tycho AI develops autonomous vehicle systems that integrate AI, robotics, and advanced control technologies for defense and commercial applications. The company is hiring Autonomy Engineers specializing in state estimation, perception, and motion planning, as well as Software Engineers to build embedded systems and software frameworks for autonomous systems.
View all jobs at Tycho AILikely interview questions
- Describe a computer vision or robotics ML project you've deployed—what were the key optimization challenges and how did you address them?
- How have you approached model compression or quantization in a production system, and what trade-offs did you make?