Jenna is an expert in the Computer Vision, Machine Learning, and Semiconductor fields across Europe. Jenna is based in our Brighton office and can be contacted on +44 (0) 1273 957888, email@example.com or you can connect with her on LinkedIn here https://www.linkedin.com/in/jennathompson/
“I recommend Jenna because she is different from the others. The very thing that makes her different is her big and sincere heart for helping people. I also recommend her because of what I see in her…sincere, persevering, efficient, excellent, reliable, trustworthy, friendly and approachable. I was so blessed working with her and I appreciate everything that she has done for me. I believe that she will be able to help a lot of people if people will allow her to help them.” August, 2016. Interconnect Development Expert, Texas Instruments.
Jenna specialises in Computer Vision & Machine Learning – OpenCV, MATLAB, Simulink, ect, as well as Semiconductor – ATE Test Development, RFIC, Mixed-Signal etc.
Here, Jenna gives us an overview of a Computer Vision & Machine Learning role, by answering some frequently asked questions:
- How much on average do Computer Vision / Machine Learning engineers earn?
The demand for candidates with this skill set is extremely high in Europe, so competitive salaries are expected. Although, salary is still very much dependent on the location of the roles and the background of the candidate.
To put this into context, we are seeing candidates with a PhD from a top university, and with a strong publications record, securing offers (in London) in excess of £60,000. Candidates who already hold a few years of experience in the industry or research, should expect to secure salaries upwards of £70,000.
London is an expensive city to live in, so often you will find salaries here are higher than other cheaper locations. Ultimately, the majority of companies know they have to compete for the best talent so will pay a fair salary for the location you are based in.
- What qualifications will I need?
As a general rule, a MSc in Computer Science, Applied Maths or similar is typically a necessary qualification to have before finding a position in AI. Whether you also need a PhD, will depend on if you are interested in pursuing a pure research role.
If you are motivated to work closer to product development or applying Machine learning/Computer Vision to real applications, an MSc is a sufficient gateway into positions in this industry.
- Do I need specific skills and attributes?
As the field of Machine Learning and Computer Vision is so broad there are several sub-disciplines within this, so skills that are required very much depend on the area in which you want to specialise. For example, Autonomous Driving, Medical, Image Processing, FinTech, Camera Sensors, to name a few.
Having knowledge of Deep Learning frameworks such as TensorFlow and Caffe are highly desirable as well as a strong grasp of at least one programming language such as C++ or Python. Most positions are grounded in mathematical fundamentals and will be related to developing algorithms (such as vision-based algorithms). So, having a strong mathematical capability will help candidates stand out.
Most clients across Europe would look for strong communication skills in English as well as the ability to work well in a team.
- What roles can I transition from?
Most engineers who have come from a background in applied mathematics will be able to transition into a role in Machine Learning / Computer Vision. We also see engineers who have previously worked in Computer Graphics securing positions in this field.
- Why do Computer Vision/Machine Learning roles exist?
Most organisations recognise that AI is no longer just a futuristic concept, as we are increasingly moving towards the creation of technology that can understand and mimic human behaviour.
Computer Vision / Machine Learning Engineers play a fundamental role in developing complex algorithms that will be at the core of the technology used in security, autonomous driving, medical, FinTech and many other applications.