Skip Portfolio Projects. Do This Instead.

The role of the machine learning engineer is being rewritten in real time. AI coding assistants are absorbing parts of the day-to-day, planning and evaluation are eating up more of the week, and the lines between machine learning engineer, AI engineer, and data scientist are blurrier than ever. For anyone working in data and AI — or trying to break in — this shift changes what skills are worth investing in, what employers actually screen for, and how interviews are run. What's still worth learning? What does a competitive portfolio look like? And how do you stand out when a thousand applicants are using bots to apply? Marina Wyss is a Senior Applied Scientist at Twitch (an Amazon company), where she builds production AI and machine learning systems across content understanding, recommendations, and forecasting. She came into the field from a non-traditional background — a political science undergrad and a Master's in social data science in Berlin — and has held machine learning roles at Coursera and a Berlin-based statistical consultancy along the way. Outside her day job, Marina runs a popular AI/ML YouTube channel and weekly newsletter, and coaches people transitioning into machine learning from non-traditional careers. In the episode, Richie and Marina explore how AI is reshaping the machine learning engineer role, the shifting balance between coding and planning, why evaluation matters more than ever, the differences between ML engineer, AI engineer, and data scientist roles, how to break into the field from a non-technical background, what makes a strong portfolio project, the hiring process at big tech, how to prepare for technical interviews, networking strategies that actually work, what success looks like in your first few months on the job, and much more. Find DataFramed on DataCamp https://www.datacamp.com/podcast and on your preferred podcast streaming platform: Apple Podcasts: https://podcasts.apple.com/us/podcast/dataframed/id1336150688 Spotify: htt