Exceptional Promise(junior software test engineer) - Advice Needed

Stage 1
1

I’m preparing the Global Talent Visa (Digital Technology) application and would appreciate advice on the strength of my evidence and how to position it.

Simple Background

  • Around 3 years work experience as a graduate/junior software test engineer in FAANG and small company across Asia and the UK.
  • Undergraduate degree in Literature, followed by two Computer Science master’s degrees in the UK (the first at a top 10 university and the second at an Oxbridge university)
  • UK Skilled Worker visa for 2 years so far.

Main Criteria (MC)

MC1 – Recognition (Media)

  • Featured by a women’s career magazine in Asia, sharing my transition from literature to tech.
  • Evidence: article link and screenshots.

MC2 – Recognition (UK Higher Education Institution)

  • Invited multiple times (online and onsite) to speak about studying and careers as an alumnus at a top UK university.
  • My name, photo, and quotes were used in university promotional materials and on the university website.
  • Evidence: invitations, staff feedback, promotional materials.

MC3 – AI-Related Leadership / Recognition

  • AI Ambassador organising AI workshops and promoting AI initiatives on an Oxbridge campus.
  • Winner of an Oxbridge AI hackathon.
  • Potential evidence related to Google Developer Group (GDG) activities.
  • Oxbridge scholarship for an AI conference.
  • Evidence: recommendation emails, LinkedIn posts, GitHub repositories, event materials.

MC4 – Exceptional Investment & Recognition

  • Salary plus substantial employer sponsorship, including a 5-year Skilled Worker visa, external professional training, and Master’s tuition support. Employer is a UK Queen’s Award-winning company.
  • First and only person in my team to achieve two ISTQB certifications.
  • Delivered presentations to the CEO and buyers/stakeholders.
  • Evidence: payslips, contracts, sponsorship documents, contribution summaries.

Current employer invested £31k on me in 2024–2025 (excluding base salary and employee benefits).

Optional Criteria (OC)

OC1.1 – Innovation (Computer Vision for VR)

Previous role in VR device testing involving a robot I designed for automated stress testing, workshops, presentations, and support for product release. Limited product details due to confidentiality.

  • Evidence: product news, small recommendation letters.

OC1.2 – Innovation (Automation)

Current role where I transformed manual testing into automation, achieving approximately 90% time reduction. Limited product details due to confidentiality.

  • Evidence: CEO appreciation email, presentation photos, GitHub repositories, reference letters, demonstrations to buyers/stakeholders.

OC1.3 – Innovation (ML Research Presentation)

Previous FAANG role where I presented my Master’s machine learning project to the ML team, with indirect relevance to product innovation. Limited product details due to confidentiality.

  • Evidence: presentation screenshots, recommendation letters.

OC3.1 – Project Contribution

Volunteer tester for a European national train ticketing platform. Millions users per month.

  • Evidence: recommendation from project owner and work samples.

OC3.2 – Mentorship

Provided advice via LinkedIn to students applying for UK Computer Science courses and careers, and they got the offers.

  • Evidence: LinkedIn messages from students seeking guidance, and appreciation message after they got the offers.

OC3.3 – Mentorship / Community Contribution

Co-authoring a global career and tech guidebook with 49 contributors, mainly from Silicon Valley. Far from publishment.

  • Evidence: screenshots and contribution records.

Recommendation Letters

  1. Current manager at my UK employer.
  2. Former colleague from a big tech company in Asia.
  3. Professor from an Oxbridge university.

I’d really appreciate feedback on:

  1. Whether these examples are strong enough for MC and OC.
  2. Which evidence appears weakest or redundant.
  3. I found it hard to measure my impact as a tester, especially when my work were small parts at big tech companies.

My Concern

I feel that I may still be somewhat junior, without major technical contributions at industry level yet. Most of my evidence is based on softer impact areas, such as experience sharing, university hackathons.

CC: @Raphael @Akash_Joshi thank you! : )