#362 How to Have a Machine Learning Career in 2026 | Marina Wyss, Senior Applied Scientist at Twitch

2026-06-01 · Source: DataFramed · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Novice, extended

Summary

Marina Wyss, a Senior Applied Scientist at Twitch, details the evolving Machine Learning (ML) engineer role, noting a significant shift from 60% coding time to increased planning, scoping, and evaluation due to AI coding assistants. The role now demands stronger stakeholder alignment, proficiency in evaluating non-deterministic AI systems, and continuous, business-driven learning. Wyss clarifies distinctions: AI engineers build on pre-trained models, while ML engineers develop models from scratch, with ML engineering increasingly encompassing AI engineering skills. She emphasizes foundational Python and intuitive math skills, alongside building a portfolio of real-world projects for clients like non-profits. The hiring process involves varied technical screens and comprehensive onsite interviews, requiring extensive preparation tailored to specific team problems. Networking and proactive engagement are crucial for career entry and advancement.

Key takeaway

For Machine Learning Engineers navigating career shifts, prioritize developing strong stakeholder alignment and robust evaluation strategies for non-deterministic AI systems. Focus your continuous learning on business problems rather than memorizing all models, and build a portfolio showcasing real-world impact. When interviewing, proactively research target teams and prepare by designing solutions to their specific challenges, as this thoroughness significantly boosts your chances.

Key insights

The ML engineer role is shifting from coding to strategic planning and evaluation, demanding new skills for AI-driven development.

Principles

• ML engineering now supersets AI engineering.
• Business needs should drive learning paths.
• Evaluation is critical for non-deterministic AI.

Method

To build a strong portfolio, identify a real-world problem for a non-profit, small business, or hobby group, then design and implement a solution, tracking measurable impact and navigating practical constraints.

In practice

• Ask interviewers about coding round format.
• Prepare for interviews by designing relevant systems.
• Engage with industry figures on their work.

Topics

• Machine Learning Engineering
• AI Engineering
• Career Development
• Technical Interview Prep
• Stakeholder Alignment
• AI System Evaluation
• Portfolio Projects

Best for: Machine Learning Engineer, AI Engineer, AI Student

Related on AIssential

• [D] Monthly Who's Hiring and Who wants to be Hired? — The ML job market features diverse roles, from production NLP to AI agent enhancement and general software engineering.
• Luis Serrano + Josh Starmer Q&A Livestream!!! — Persistent effort and foundational understanding are key to navigating the fast-evolving AI landscape.
• AI/ML Interview Questions & Answers You Should Prepare Before Applying — AI/ML interviews prioritize core concept clarity, problem-solving, and practical understanding over encyclopedic knowledge.
• STEM PhD's transitioning to MLE/Data [R] — A postdoc in an ML lab is a proven path for STEM PhDs to transition into MLE/Data Science roles.
• I Quit My $130,000 ML Engineer Job After Learning 4 Lessons — Big Tech's stability and mature systems can stifle innovation and personal growth for action-oriented engineers.
• Key Strategies for Building Effective Machine Learning Systems — Effective ML system development prioritizes iterative refinement, data quality, and appropriate model complexity for robust, real-world performance.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by DataFramed.