From Lab to Launch Building a Career Beyond Academia

This deck was prepared for MBZUAI Global Career Tech Talk on Oct. 27, 2025. Preparing slides forced me to distill years of lessons into a single narrative, and I’m excited to share it with the next generation of AI talent.

It covers the following topics

* My Career Journey
* Deciding your first / next job
* Succeeding in the first 90 days
* Building a career, not Just a job
* Future of AI, CS, Robotics, andData Science careers

1. From Lab to Launch
Building a Career Beyond Academia
Yunyao Li | Director of Machine Learning
MBZUAI Global Career Tech Talk
10/27/2025
@yunyao_li

2. My Career Journey by Time
•Ph.D.
•Research Staff
Member
2007
•Research Manager
2014
•Sr. Research
Manager
2018
•Principal Research
Staff Member
2019
•Distinguished
Research Staff
Member
2021
•Head of Machine
Learning, Apple
Knowledge Platform
2022
•Director of Machine
Learning, Adobe
Experience Platform
2023-

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My Career Journey by Numbers
Books Publications
2 120+ 50+
Patent granted / pending
Teams built Product impacted
3 40+ 20+
Awards

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My Dissertation

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Highlights in 2024: Adobe Experience Platform AI Assistant
▪Answers user questions about product
knowledge and operational insights
▪Purposely-built question answering
pipeline involving in-house models and
public LLMs
▪Generally Available since June 2024

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Highlights in 2025: Adobe Agent Orchestrator
https://business.adobe.com/products/experience-platform/agent-orchestrator.html

7. Outline
▪Deciding your first / next job
▪Succeeding in the first 90 days
▪Building a career, not Just a job
▪Future of AI, CS, Robotics, andData Science careers

8. Deciding Your First / Next Job

9. How to Decide Your First / Next Job?
Work Team
LeadershipCompany
Impactful
Innovative
Balanced
Strategic
Visionary
…
Collaborative
Independent
Inclusive
Stable
Agile
…
Powerful
Supportive
Visionary
Inspiring
Down-to-earth
…
Trusted
Influential
Ethical
Transformative
Impactful
…

10. An Example
Work Team
LeadershipCompany
Impactful
Innovative
Balanced
Strategic
Visionary
…
Collaborative
Independent
Inclusive
Stable
Agile
…
Powerful
Supportive
Visionary
Inspiring
Down-to-earth
…
Trusted
Influential
Ethical
Transformative
Impactful
…

11. Another Example
Work Team
LeadershipCompany
Impactful
Innovative
Balanced
Strategic
Visionary
…
Collaborative
Independent
Inclusive
Stable
Agile
…
Powerful
Supportive
Visionary
Inspiring
Down-to-earth
…
Trusted
Influential
Ethical
Transformative
Impactful
…

12. Succeeding in the First 90
Days

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From Academia to Product
Shift from depth to iteration:
Ship, learn, and repeat
Prioritize impact over
perfection
Communicate simply and
clearly

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Challenges & Preparations
Challenges Preparation
Information overload
New tools & workflows
Adapt to pace & culture
30-60-90 day plan with measurable
objectives
Self-learning + ask good questions (do
your homework first!)
Study the ”unspoken rules”

15. How to Build Credibility & Trust
DEMONSTRATE
COMPETENCY
WORK HARD BUILD RELATIONSHIP

16. The Relationship Pyramid
Credit: https://www.linkedin.com/pulse/coach-mentor-sponsor-3-essential-roles-you-want-need-nora-osman/

17. The Relationship Pyramid
Credit: https://www.linkedin.com/pulse/coach-mentor-sponsor-3-essential-roles-you-want-need-nora-osman/
Be consistently competent, reliable, and ambitious.
→ Inspire others to put their credibility behind your
success

18. Building a Career,
Not Just a Job

19. Industry Research vs. Product
Research
Discovery and (potentially) long-term
impact
Applied Research
Exploration and near-term impact
Product
Execution and immediate impact

20. Research
Discovery and long-term impact
Applied Research
Exploration and near-term impact
Product
Execution and user impact
Industry Research vs. Product
The boundaries are blurry …
Practical problems can be equally compelling as fundamental
ones, and often lead in turn to the discovery of new
fundamental science.
- Stephen Quake
Credit: https://archive.nytimes.com/opinionator.blogs.nytimes.com/2009/02/17/the-absurdly-artificial-divide-between-pure-and-applied-
research/

21. Changing “T-Shaped Skills”
Credit: https://www.nngroup.com/articles/return-ux-generalist/
Initial focus: 1-3 years
Overtime

22. Typical Job Profiles in an AI/ML Organization
Role Key Responsibility Typical Credential
Software Engineer Turns ideas into scalable, reliable systemsBS / MS in CS / engineering
Machine Learning EngineerDeploys and maintains ML models in production
BS / MS / PhD in CS /
engineering
Data Scientist Unlocks insights and guides decisions with dataMS / PhD in Statistics / DS
Applied Scientist
Conducts and translates cutting-edge AI/ML research
into practical solutions
PhD in CS
Research Scientist Pushes the boundaries of AI/ML methods PhD in CS
Engineering Manager
Ensurespeople thrive, research becomes reality, and
systems scale responsibly.
BS/MS in CS / engineering
Product Manager Defines vision and priorities for AI productsBS in CS/engineering + MBA
Program Manager Orchestrates execution across teams and projectsBS/BA in business / engineering
Check out job postings to learn more!

23. Career Progression
Credit: https://www.linkedin.com/pulse/from-junior-senior-software-engineering-career-ladder-ivo-c80gf/
Junior-Level
•Be curious and ask good questions
•Focus on code quality
•Embrace feedback
Mid-Level
•Take ownership of your work
•Improve problem-solving skills
•Mentor Juniors
Keep a growth mindset!
Senior-Level
•Think strategically: short-term vs. long-
term
•Lead by example
•Focus on impact

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Important Soft Skills
Communication
clear, concise,
audience-aware
Collaboration
cross-team, cross-
discipline
Adaptability
learn fast, handle
change
Networking
build influence
Problem-solving +
strategic thinking
create impact

25. Future of AI, CS, Robotics,
andData Science Careers

26. Future of AI, CS, Robotics, andData Science Careers
Remains the foundation; life-long learning required;
learns to build systems not just write papers.
CS
Growing demand for applied AI talent across
industries; domain knowledge helps!
AI
Cross-disciplinary demand:
robotics + AI + hardware + control systems.
Robotics
Increasingly paired with ML engineering forgreater
impact; goes beyond analytics.
Data Science

27. Life is a marathon!

28. Keep Learning …

29. Generated w/ Adobe FireFly

30. H. V. JagadishMary FernandezShiv Vaithyanathan Sekar Krishnamurthy
Michelle Zhou
Laura Haas Ruoyi Zhou
Ihab IlyasRonald Fagin
Sriram Raghavan
Anjul Bhambhri
Jeff Welser
Cheenu VenkatacharyDrago Radev Manas GargAya Soffer
and many other amazing people …