Startup Life: Unscripted #12 with Julia Tan, Machine Learning Engineer at black.ai

Welcome to Startup Life: Unscripted, a TNG Media newsletter as part of The Nudge Group, where we feature candid conversations with startup operators about their career journeys and experiences. If you received this email as a forward, you can read all our past interviews and subscribe right here.

From Mechatronics to Machine Learning Magic: Julia Tan's Trailblazing Journey at black.ai

Welcome back to Startup Life: Unscripted! Today, we're thrilled to introduce you to Julia Tan, a Machine Learning Engineer at black.ai, a cutting-edge research and development start up in the artificial intelligence space.

Blending her foundational knowledge in Mechatronics Engineering with the vibrant pulse of startup dynamics, Julia exemplifies agility, innovation, and tenacity. At black.ai, her contribution is instrumental in sculpting the company's technological frontier.

In our conversation, Julia delves deep into the multifaceted world of AI and underscores the essence of teamwork, the drive for constant up-skilling, and the urgency to keep pace with the swift evolutions in AI.

Key interview takeaways:

💼 Journey and transitions: Julia reflects on her intriguing transition from Mechatronics Engineering to the forefront of AI at black.ai. She sheds light on the synergy between academic paradigms and real-world challenges in AI.

💡 On black.ai's culture: Julia gives us a look what it’s like working at a cutting-edge AI startup and walks us through a day in her life at black.ai.

🎉 Passion and drive: Discover what keeps Julia inspired! She shares her zeal for harnessing AI to tackle real-world problems, her joy in collaborative projects, and the essence of working on groundbreaking technologies.

🚀 Guidance for AI aspirants: For those looking to make a splash in the AI startup realm, Julia offers invaluable advice: Be curious, proactive, and never stop learning. Embrace challenges and remain flexible in an ever-evolving field.

Hey Julia, great to have you here with us today! So, you're juggling your work with AI and computer vision at black.ai and Mechatronics Engineering studies. How do the two worlds collide for you?

Studying Mechatronics Engineering has definitely trained me to view problem solving through the lens of systems design, which has directly influenced how I approach my professional work.

There is a strong focus in Mechatronics on how different components of a complex system interact and integrate with each other, and this is something that I have found to be particularly applicable to working in a startup, where systems are still new and evolving every day, and there is typically more closeness in working responsibilities between individual teams.

In the other direction, working while still completing my studies has led to me putting less importance on the specific content in my courses, as it can sometimes feel a bit out of touch from what is currently going on in industry, and more so on the processes behind how to approach problems and self-driven learning in general.

What inspired you to dive into the world of AI and computer vision, and how did you land your role at black.ai?

I’ve always been interested in the world of AI since I started university, and took every opportunity that came my way to learn more in research projects and internships. I was particularly inspired by the creative possibilities of AI beyond what conventional computer programming could achieve.

Computer vision is such a big field in AI today, and opens up the capacity for real-world systems that can accurately and effectively capture the true environment that people and machines are operating in. This real-world utility is a fundamental aspect of what motivated me to take a role in the research team at Black.ai.

I first heard about the interesting work happening at Black.ai through a LinkedIn message, and ended up successfully going through the interview process for an open position after that initial connection.

Working in machine learning and doing it remotely sounds fascinating! How do you keep the team vibe strong when you're not in the same physical location? Can you walk me through a day in your shoes.

My days typically start with a team standup, which helps to get everyone on the same page, irrespective of physical location. These daily stand-ups, along with other sprint related meetings, allow for joint project planning and a clear organisation of tasks/responsibilities.

As a result, there are defined expectations and a shared understanding of what everyone is working on at the present time, making collaboration a lot easier. Having tools such as Slack and Google Meet also makes it feel less isolated working remotely, as I am able to reach out to my colleagues at any time during the day.

Startups are always buzzing with change and energy, right? How has that lively environment shaped the way you work and learn? Got any cool stories about a time you had to think on your feet or pick up something new super quickly?

I would say that this dynamism has influenced how I perceive my own role in a team, as I always try to make sure that I am constantly learning new aspects of the business, and that vice versa my own knowledge isn’t being siloed with me.

As a team we are continuously participating in knowledge sharing, which makes adapting to changing situations a lot easier as there is less of a constraint between tasks and the people doing them. An example of this is when I had to learn how to set up and calibrate a new site.

Thanks to the knowledge sharing, I already knew how the different aspects of the system worked and functioned together, which allowed me to quickly pick up the new site, and immediately move onto improving the calibration process of it.

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How do you navigate the occasional ambiguity or rapid changes that are part and parcel of working at a startup? Could you share a tip or two for people who might be new to the startup environment?

Personally, I’ve found that navigating ambiguities is much easier when you understand the overarching objectives or goals of the team, as this provides direction when the next steps are not necessarily obvious. For dealing with rapid changes, I’ve found that working in smaller tasks helps make things more manageable, allowing for greater flexibility and the capacity for making “on-the-go” edits to the original plan.

I also think that it’s important to be aware of your own individual capacity, which helps in being able to say yes or no to things when there are competing priorities. For people who might be new to the startup environment, it can sometimes feel like you should say yes to whatever people are asking from you, but I think it’s actually more helpful to everyone involved if you don’t promise more than you have capacity for.

Can you share a bit about your previous experience working on space robotics at a Sydney-based startup? How has this experience helped prepare you for your role?

My previous experience involved building bespoke robots for NASA JPL, which were intended to function as collaborative robots in the cleanroom environment. Through this experience, I learned how to design for customer needs, as the product was being built from scratch, meaning there was no set precedent to follow. I also learned how to work in short timelines and with limited resources, coinciding with building my capacity to “wear multiple hats”.

With AI and machine learning being such rapidly evolving fields, how do you stay up-to-date with the latest advancements and integrate them into your work?

I like to stay up-to-date by reading the latest research papers in my field, including following the output of recent conferences (where papers are usually first published). I’ve also found it valuable having friends, colleagues and mentors who are in the field as well that share interesting content with me, whether it be from articles or even YouTube videos.

In a similar vein, I think there’s merit in taking note of things that you find interesting, but not necessarily immediately relevant to your work right now, as they could potentially become able to be integrated into future work. On top of that, finding space to discuss such new advancements with the rest of my team has been an important way of inspiring ideas regarding where and how they might be useful for our work.

To wrap up, if you were to share a golden nugget of advice with budding enthusiasts looking to make a splash in the AI and machine learning startup scene, what would it be? And what do you think are the must-have skills or experiences to thrive in this field?

I would recommend not to wait for a job to start learning and up-skilling yourself in the fields you are interested in. You can do this by starting a personal project, or connecting with a research group at your university (if you are still a student).

There are so many resources available online now, including heaps of open-source AI frameworks and tools, which you can use freely for your own learning. In line with this, I think that being flexible, and having the curiosity and willingness to learn new things by simply trying on your own is really valuable in this industry, especially as it continues to move at such a fast pace.

From the Startup Life team

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