About the job
About Us:
Innatera is an innovative and rapidly expanding Dutch semiconductor company dedicated to developing ultra-efficient neuromorphic processors designed for edge AI applications. Our advanced microprocessors emulate the brain's data processing capabilities, offering exceptional performance for complex sensor analytics with a staggering 10,000 times better efficiency per watt than traditional solutions. Our technology is pivotal for enabling next-generation applications across various domains, including IoT, wearables, embedded systems, and automotive industries.
We are currently seeking a talented Embedded Machine Learning Engineer to join our Algorithms team, which is at the forefront of creating cutting-edge AI algorithms that drive our neuromorphic technology. The ideal candidate will possess a proven track record in the development and deployment of embedded ML solutions within commercial environments.
YOUR ROLE INCLUDES:
Spearheading the design and implementation of cutting-edge machine learning algorithms tailored for real-world applications in hardware and software.
Developing and optimizing embedded ML models utilizing PyTorch and NumPy, and deploying them within embedded systems using C and Python.
Conducting analysis, benchmarking, and refinement of embedded ML pipelines to enhance latency, memory efficiency, and power optimization.
Advancing explainable AI methodologies that yield actionable insights into model behavior, fostering trust and reliability for our clients and end-users.
Expanding our ML toolkit through research and integration of the latest techniques, ensuring we remain at the cutting edge of AI evolution.
Establishing scalable and reproducible workflows that exemplify excellence in embedded ML development.
Collaborating with a diverse team comprising hardware engineers, data scientists, and software developers to deliver integrated software and hardware applications.
Providing essential algorithmic insights to hardware teams for seamless integration and performance enhancement.
Facilitating the integration of ML models with hardware AI accelerators and neuromorphic processors.
Engaging with internal teams and clients to innovate solutions that meet industry demands.

