Analog Devices is building a pioneering organization focused on advancing the next generation of Edge AI compute and intelligent sensors. They are looking for an Edge AI/ML Scientist to develop and test lightweight learning models on state-of-the-art edge compute platforms.
Requirements
- Solid understanding of core machine learning models and concepts, including CNNs, RNNs, Transformers, continual learning, and on-device retraining.
- Hands-on experience with major ML frameworks such as PyTorch or TensorFlow, and deployment tools like TensorFlow Lite, TVM, or ONNX.
- Strong analytical mindset with the ability to balance performance, power, and memory tradeoffs.
- Bonus: Exposure to neuromorphic computing, analog compute, or embedded systems development.
- Big Advantage: Experience with SNN framework or converting ANN to SNN, advanced optimization, and compilation techniques
Responsibilities
- Develop lightweight learning models suitable for constrained edge devices operating at ultra-low power with limited memory.
- Evaluate trade-offs in model accuracy, memory footprint, compute cost, and latency across different edge platforms.
- Collaborate with Compute architecture team to align model structure with analog or neuromorphic constraints, including weight updates and limited precision.
- Assist in benchmarking emerging compute platforms (startups and internal solutions) on real-world µAI tasks.
- Evaluate new optimization, distillation, and compilation approaches for edge deployment.
- Document results clearly to inform architectural decisions and guide startup engagement strategies.
Other
- Master’s or PhD in Artificial Intelligence, Electrical Engineering, Computer Science, or a related field with a strong focus on machine learning or embedded AI.
- Strong collaboration and documentation skills, with a proactive attitude toward working in a fast-paced, cross-disciplinary environment.
- Required Travel: Yes, 10% of the time
- The expected wage range for a new hire into this position is $108,800 to $149,600.
- This position qualifies for a discretionary performance-based bonus which is based on personal and company factors.
