Design and optimize machine learning inference pipelines for edge devices. Develop high-performance runtimes for GPU and ARM architectures. Work closely with research and platform teams to deploy production-ready AI systems.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
About Neuramorphic
Neuramorphic is building the infrastructure for the next generation of intelligent systems. Our NeuraTensor™ SDK enables developers to run AI models directly on edge devices such as cameras, robots, and industrial systems—without cloud latency or privacy risks.
From autonomous vehicles to industrial robotics, our technology allows real-time AI inference where milliseconds matter.
We are looking for a Lead ML Systems Engineer to help design and optimize the core inference engine powering the NeuraTensor platform.
Responsibilities
- Design and optimize machine learning inference pipelines for edge devices
- Develop high-performance runtimes for GPU and ARM architectures
- Optimize deep learning models for ultra-low-latency inference
- Integrate model pipelines with frameworks such as PyTorch
- Improve memory efficiency, GPU utilization, and power consumption
- Work closely with the research and platform teams to deploy production-ready AI systems
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Requirements
- 5+ years of experience in machine learning systems, AI infrastructure, or high-performance computing
- Strong programming skills in C++ and Python
- Experience with GPU programming (CUDA preferred)
- Experience with ML frameworks such as PyTorch or TensorFlow
- Familiarity with ONNX, TensorRT, or ML compilers
- Experience working with Linux-based systems
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Nice to Have
- Experience with edge AI or embedded systems
- Experience with ARM architectures or NVIDIA Jetson devices
- Experience building ML runtimes or compilers
- Background in robotics, computer vision, or real-time AI systems
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Job Types: Full-time, Part-time
Benefits:
- Relocation assistance
Application Question(s):
- Do you have professional experience with C++ for performance-critical systems?
- Work Location: In person
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