My name is Kai and I am originally from London, UK. I studied Maths and Physics during my undergrad, but always knew that I wanted to make the leap from science to technology. Unfortunately, I did not learn many practical software or hardware engineering skills during my degree, which mainly focused on theoretical physics and pure mathematics. I therefore decided to pursue a master's degree in Machine Learning, which provided me with a great opportunity to both build on top of my theoretical background and learn some tangible skills that I could take to the job market. My current position is working as an applied AI researcher in autonomous robotics. I deal mainly with building, developing and implementing state of the art machine learning algorithms and pipelines for robot perception.
These days, every company under the sun is trying to incorporate AI into their tech stack. Depending on your desired role, it can be very possible to find a job in the space with only a Bachelor's degree. For example, data science positions or software engineering/MLOps roles often do not require an MSc. These jobs focus more on analysing the output of ML models (data science) or building the infrastructure to build, train and deploy models (MLOps). However, if you are seriously considering a career in AI or machine learning and want to work on cutting edge algorithms and technologies, pursuing a postgraduate degree in the subject would be very beneficial.
Here I will list a few possible career options that one can pursue in the field of Machine Learning and AI. Please note that the list is not exhaustive, and the requirements will vary depending on the position, but hopefully it will give you an idea of the different paths that are out there.
In my personal experience, I found that looking at more than just the “known” companies was extremely helpful when finding a job. A lot of start-ups will have much more interesting opportunities than big tech companies and are often willing to compromise on experience requirements if you are a promising candidate.
I had a very specific idea of what type of job I wanted so I only ended up applying to a handful before finding one that was a good fit, but even then, it took a few months to find the opportunity. I found my current company by Googling “cool reinforcement-learning start-ups" and looking at the careers page of each one that came up, so don’t be afraid to get creative with how you find jobs to apply for. Other than that, I found that LinkedIn offers an easy way to find new job opportunities, just make sure you check the list regularly to apply for listings as soon as they come up – I've found that you get a better response rate that way.
Don’t panic if you don’t find a job right away. Even if it seems like the job market is saturated, there will always be new, interesting opportunities posted, given how popular and ubiquitous this field is within technology. Nothing more than being patient.
In conclusion, finding a job can be a daunting process, but in a field like machine learning there are always interesting options arising. If you are interested in more research-based roles, consider applying for a master’s or PhD, but if your interests lie in software engineering or data science, programming ability, self-learned courses and enthusiasm go a long way!