Machine Learning Engineer

The Machine Learning (ML) Engineer's primary role is to implement and optimize machine learning algorithms for BrainChip’s Akida Neuromorphic System-on-Chip (NSoC). This role requires a strong practical proficiency in ML, particularly in embedded AI. The ML Engineer will work on applications such as computer vision, audio processing, Language Models and real-time systems, contributing to the integration of ML solutions into the Akida platform.

Essential Job Duties and Responsibilities:

• Implementing and optimizing ML algorithms for deployment on embedded systems.
• Working closely with the research team to translate ML models from theory to practice.
• Developing and maintaining efficient code in Python, C, and C++ for real-time systems.
• Staying current with advancements in ML, embedded AI, and related technologies.
• Collaborating on ML algorithm/hardware co-design tasks to enhance system performance.
• Debugging and Benchmarking software to ensure optimal performance on the Akida hardware.
• Interfacing with customers to understand their needs and provide technical support for ML applications.
• Contributing to the development of the Akida software stack and toolchain.

Qualifications:

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Education/Experience:

• Bachelor’s Degree in Computer Engineer, Computer Science, Electrical Engineering, or a related field with 5+ years of experience; or a Master’s Degree with 3+ years of experience.
• Strong background in machine learning and embedded AI.
• Proficiency in Python, C, and C++.
• Experience with real-time operating systems (RTOS).
• Understanding of computer architecture principles.

Preferred Qualifications:

• Experience in developing ML applications for embedded systems.
• Multi-project experience in computer vision, audio processing, and sensor fusion.
• Experience with ML frameworks such as TensorFlow, Keras, and PyTorch.
• Familiarity with Docker and Git.
• Knowledge of Scrum/Agile software development methodologies (e.g., Jira).
• Evidence of creativity and innovation in previous projects.