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Job description
Ekkono’s product is a machine learning library for embedded systems developed in C and C++. In your role as Embedded Developer you will become an essential part of the development team. Ekkono is currently on a growth journey where they are expected to increase with both personnel and product. You will be part of this journey while developing Ekkono's core product.
Ekkonos office is based in Varberg and they apply hybrid/remote work with colleagues located in Borås, Malmö and Göteborg to name some examples. For this position, it’s preferred that you are able to work from Ekkonos office in Varberg a couple of times a week (depending on where you are located).
In this recruitment Ekkono is collaborating with Randstad Technologies. For more information, please contact Recruitment Consultant Julia Christensson at [email protected]. Last date for application is 2023-04-17. The position may be filled before the last application day so please apply as soon as possible.
Responsibilities
In this role you will:
- Implement new machine learning algorithms from scratch
- Optimize code in regards to memory usage and speed of execution
- Create, maintain and test generic software aimed at running on multiple platforms and architectures
- Work with industrial customers to create custom solutions for their specific needs
Qualifications
- Very good knowledge and experience of programming in C
- Good knowledge in one or more of the following programming languages: C++, Python
- Experience of cross platform and embedded development
- Knowledge in automated test platforms, unit testing, and/or CI platforms
- Experience in leveraging hardware acceleration for graphics processing, machine learning or DSPs
- Previous experience in machine learning.
- Familiarity with real-time operating systems RTOS
- Fluent in Swedish and English, both verbally and in writing
About the company
Ekkono is born out of seven years of machine learning research. More precisely in the area of high performance computing and predictive analytics. The result is a resource efficient small-footprint solution which can run most applicable machine learning algorithms on small platforms. We deliver an edge computing platform for connected things packaged in a Software Development Kit.