From Physicist to Machine Learning Engineer Justin Chen transitioned from academia to machine learning after receiving his PhD in Physics from Rice University. He initially struggled to showcase his skills to the industry, but shifted his focus to highlighting mathematical methods and coding work. Chen emphasizes the importance of being able to communicate code with others as a key skill for success in machine learning engineering. He worked on various projects at Manifold AI, including end-to-end ML pipelines, and developed expertise in handling sensitive data in the healthcare space. Chen advises starting with basic models, focusing on relevant metrics, and identifying subject matter experts to narrow down feature sets. In his current role at Google, he focuses on speech recognition and audio processing, using techniques like NLP and explainability to address challenges. Chen stresses the importance of monitoring models regularly, having performance metrics, and having a human in the loop to detect bias and improve fairness. He also notes that working for a startup can provide excitement and flexibility, but may not offer the same level of resources or growth opportunities as a bigger company.