If you want a machine learning-focused role in the Singapore banking sector, you might well end up in a so-called innovation lab, where banks partner with startups to develop potential new tech products. Citi’s Singapore lab, for example, houses most of the bank’s jobs that require skills in machine learning and other emerging technologies.
But while innovation labs have sprung up island-wide in recent years (at banks such as HSBC, OCBC and Standard Chartered), the projects they work on are not always adopted by the wider business. There’s still a slight ‘moonshot’ stigma about being based in a lab.
In contrast, machine learning (ML) jobs in pure-play tech firms in Singapore are generally critical to the business. Moreover, Google, one of Singapore’s most expansionist foreign tech firms and a leader in ML development globally, is currently in need of ML expertise, according to its careers site.
Machine learning jobs at Google in Singapore focus on developing the technology within the firm’s core products. If you secure the current ML software engineer opening at Google Pay, for example, you’ll be building new ML models, data pipelines, features and metrics for the company’s colossal payments platform.
Another Singapore ML engineering vacancy involves building “on-device machine learning models for health and wellness”. This position is potentially all the more appealing following Google’s $2.1bn acquisition on Friday of health tracking company Fitbit, a deal that demonstrates Google’s commitment to the health sector.
Google’s Singapore office also employs ML specialists within the partner engineering team that manages TensorFlow, a proprietary API system that helps third-party developers build applications on Google’s platforms. A current vacancy for a TensorFlow ‘developer advocate’ requires “experience deploying machine learning solutions” based on the Google API or “equivalent tools”. It’s also a job where your communication skills must come to the fore. You must “love connecting with developers and speaking publicly about cutting-edge technologies on conference panels, at user groups, on blogs and with the press”.
So what’s the catch with these machine learning jobs at Google in Singapore? Not only is it already notoriously difficult to land any kind of Google job (applicant ratios suggest it’s easier to get a place at Harvard), but some ML roles at the firm come with a dauntingly high bar to entry.
For example, while having a PhD is often merely a desirable attribute for an ML position in banking, Google’s current health-focused ML software engineering vacancy lists the degree as a “minimum qualification”. This isn’t a particularly senior job either – it demands five years’ ML experience.
Whether Google will poach people from banks to fill its current crop of machine learning jobs in Singapore remains to be seen, but banking innovation labs in the city may struggle to hold onto their best ML experts over the long term. Google’s ML roles are arguably more attractive in terms of the technology, but they also pay well. A mid-level software engineer at Google in Singapore can earn an annual base salary of about S$195k, while VP developers at banks take home around $S134k, according to data from Glassdoor and recruitment agencies, respectively.
Image credit: 400tmax, Getty
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