For SWE To ML Transitioners

In this final section on preparation strategy, we focus on how to transition successfully from software engineering into machine learning. Drawing on real experiences, we highlight practical, effective ways to stand out.

• Lead with your strengths: We already bring strong system design, coding, and production skills—qualities ML teams highly value. Hiring managers often prefer engineers moving into ML over scientists moving into engineering.
• Leverage your current role: Instead of relying only on side projects, we can grow by contributing to ML efforts at work—like model deployment, pipelines, or feature engineering.
• Be selective with side projects: One well-scoped project with clear business impact is far more compelling than several small tutorials.
• Demonstrate your learning journey: Show, don’t just tell—mention coursework, hands-on coding, and real applications of ML in your work.
• Be honest and self-aware: Interviewers appreciate curiosity and a growth mindset more than perfection. Let’s share where we are, what we’re learning, and how we’re applying it.

If you want to learn even more from Yayun:

• Book 1 on 1 career guidance with her here: https://calendly.com/your-friend-yayun/1on1
• Follow her on LinkedIn: https://www.linkedin.com/in/yayunjin/