“Be proactive and utilise your network”

Ying Liu is a Machine Learning Engineer with a Ph.D. in Physics and over four years of industry experience. She is currently the Head of AI at TG0 and one of the founding Directors of the Women Coding Community in London.

Her inspiration to support women in tech came after attending AI meet-ups, where she noticed the lack of female representation. Determined to make a difference, Ying began sharing her knowledge and creating opportunities to encourage more women to enter the field.

In this blog, Ying talks about her journey and offers advice on how women can successfully transition into tech.

Hi Ying, thank you for getting involved with Women Rock! Can you tell us about your journey and what inspired you to pursue a tech career, specifically in the field of machine learning and AI?

I entered the field of machine learning relatively late after transitioning from a PhD in physics. At this time, I had limited coding experience but during my PhD I learned Python so I could develop custom software for simulations.

It was during this time I became aware of machine learning's growing popularity in physics through conferences and discussions with peers. In 2018 I attended an event at the Alan Turing Institute discussing AI's impact on life, where a speaker emphasised the rapid evolution of machine learning and the importance of joining the field quickly before being left behind.

Motivated by this, I began learning machine learning through online platforms like Coursera and eventually transitioned into the field part-time, marking the beginning of my career in ML.

What challenges have you faced in your career to date?

Finding that ‘first job’ is hard, especially as I was looking for one that offered the balance between engineering and research which was made even more challenging as I don’t come from the typical computer science or mathematics background.

It meant that I had to learn a lot on the job, including basic concepts like computing time and memory management, which are fundamental in computer science and would come more naturally to those who followed a traditional career path.

It was during this period I observed that AI meetup events are predominantly attended by men, which motivated me to begin promoting and encouraging more women to get involved in the field.

Do you feel that people with a computer science background have an advantage over people who don’t when transitioning into more industry-focused roles?

Definitely.

There is a noticeable difference in the first few years of work between those with a computer science background and those from a purely academic route. That being said, after a few years of work experience, the difference becomes negligible and having a strong scientific background has its advantages within the field of ML.

What is your proudest professional achievement?

My proudest achievement was publishing two papers as a first author. The papers focused on visual learning, specifically addressing challenges in gesture recognition with limited data and on low-power devices like microcontrollers. I developed an algorithm to sit on top of legacy AI models, allowing for efficient tuning towards new datasets without extensive retraining. These algorithms have been used and continue to be used in a commercial setting.

What advice would you give to young women or women in general who are looking to transition into tech?

Tech is a very competitive field so you need to do as much as you can to stand out.

Interview panels understand that people entering the field for the first time won’t have much commercial experience to discuss so you should invest time working on personal projects and publishing them on GitHub.

It’s also important that your coding skills are as sharp as possible. Again, the bar won’t be as high as it would be for experienced people, but participating in coding challenges is a great way to hone your skills and demonstrates during an interview that you’re eager to learn and develop.  It’s all about differentiating yourself from other people and going the extra mile.

Do you think people coming from university or PhD backgrounds should take up a research role or a research engineer role and then try to transition to become a machine learning engineer?

Maybe. In my experience, I have found that it's easier for PhD graduates to become machine learning engineers than researchers.

Machine learning research roles are harder to come by as fewer companies hire them. There’s a higher demand for machine learning engineers as the title can carry a range of responsibilities and the number of engineers in a business grows as they scale which often isn’t the case for researchers.

Moving on to the work you’re doing to help increase diversity in tech. Can you tell us a little more about the Women Coding Community and how your role with the organisation came about?

Initially I started volunteering at the London chapter of Women Who Code but unfortunately due to funding issues, the organisation closed. Without wanting to lose the community completely, I and a few other members decided to start the Women Coding Community and things took off quickly, within a few weeks we had a brand new website!

Women Coding Community runs various programs including a range of workshops to help people improve their public speaking and interview confidence as well as ones focused on more technical subjects like front and backend development, machine learning etc. We even run book clubs, writing clubs and LeetCode competitions.

The workshops are run by both directors and leaders within the organisation. We have people specialising in certain technologies, and programming languages so they host events that are relevant to their area of expertise.

How often are the events and how do people get involved?

We aim to do three events a week, which is a lot. Most of the events we run are virtual, but we aim to do an in-person event once a month. We currently have a few thousand active members but of course, not every event is relevant to everyone.

For more information on upcoming events and how to get involved, please visit our website - https://womencodingcommunity.com/ - we’re always looking for new members, speakers, volunteers, and sponsors.

What book would you recommend for our Women Rock bookshelf? 

The book I would recommend is Deep Learning by Ian Goodfellow. 

What is your favourite song?

My current favourite song is Freddie Freeloader by Jon Hendricks

Any final words of wisdom to close off the interview?

It’s important to be as proactive as possible when looking to make a career change into tech. You can achieve anything you want with hard work and patience.

Another piece of advice is to seek internal referrals. These are arguably the most effective ways to break into or transition into a career in tech. Applying directly is a numbers game and it’s hard to stand out. Having people within a business advocate for you is much more powerful.

Interviewed By Jamie Forgan