Making the switch from Software Engineer to Machine Learning Engineer (MLE) can seem daunting, especially in competitive environments like FAANG companies. Having gone through this journey myself, I understand the uncertainties that come with it—where to begin, what to prioritize, and how to maintain motivation.

In this article, I share a structured 7-step plan that helped me transition successfully, along with the challenges I faced and lessons I learned along the way.

Let’s dive in!

1. Identify Your Motivation

To make a meaningful career change, align with an area where you can contribute, take ownership, and eventually lead.
I started at Amazon’s Pricing team—not my preferred field—but the knowledge I gained proved essential when transitioning to an MLE role later.

2. Be Open to Compromises

Career shifts often involve short-term sacrifices for long-term growth. Here are some trade-offs I made:

Turned Down Google Offers Twice: Although Google offered higher pay, the roles were in Android development without ML exposure. I chose Amazon because it allowed me to work closer to ML teams, despite not initially being an ML role—a decision I still consider my best.
Delayed Promotion for 3 Years: I postponed a promotion to Senior Software Engineer because transitioning from a senior engineer to MLE would have been harder due to higher expectations.

3. Find a Supportive Manager and Team

Even if you’ve identified your goal, a supportive environment is crucial for success.

Seek ML Opportunities: Join teams where ML and software engineering work closely together. If these opportunities are unavailable at your company, explore external options.
Collaborate with an Understanding Manager: A manager who understands ML and supports your growth can accelerate your transition. Prepare a detailed transition plan and share it in regular 1:1 meetings. If your manager isn’t supportive, consider other teams or opportunities.

4. Earn Trust by Excelling in Your Current Role

In my initial role at Amazon, I went all-in, even though it wasn’t my dream job. By becoming a reliable software engineer, I earned the trust of my manager and secured the freedom to choose the projects I worked on.

This trust-building is crucial because top-performing engineers often get to select their projects, opening doors to ML-related tasks.

5. Work on ML Projects Proactively

Once you’ve found a supportive environment, start applying ML concepts through small projects:

Collaborate with ML Engineers: I contributed to AWS SageMaker pipeline improvements within my team, gaining insights into ML processes and model iterations.
Expand Your Knowledge Base: Explore broader challenges within your domain by reading industry blogs and research papers from companies like eBay and Walmart. Identify potential areas for improvement and document your ideas thoroughly.

6. Track Your Progress and Learnings

Maintain a detailed journal of your transition journey. Include technical insights, design documents, feedback, and challenges. This documentation not only helps you track progress but also serves as proof of your expertise during performance reviews.

7. Understand the New Performance Metrics

Software Engineers and MLEs are evaluated differently. In software roles, the focus is on scalable design, code quality, and project complexity. In ML roles, however, the emphasis shifts to the impact on business metrics and technical expertise.

Since ML projects often have longer development cycles, your work must demonstrate clear value aligned with business outcomes.

Bonus Tips for a Smooth Transition

Find a Mentor: A mentor within your team can offer guidance, resolve challenges, and highlight new opportunities.
Communicate Proactively: Keep your manager and team updated on your progress, challenges, and successes. Regular communication ensures continued support and alignment.
Refine Your Strategy: Continuously adjust your learning plan based on feedback and new insights.

Final Thoughts

Transitioning from Software Engineer to MLE is both challenging and rewarding. It demands strategic planning, continuous learning, and adaptability. By following these steps, you can pave the way for a smooth transition and long-term success in your new role.