Preview: Demystifying ML Interview Prep — What Actually Works

Join us for a candid, practical session on breaking into Machine Learning Engineer (MLE) roles. Hear directly from practicing ML Engineers and Researchers about what actually gets you hired—skills, signals, and preparation that work. Additionally, experience a live demo of WittyByte (https://www.wittybyte.ai/), an LLM-powered interview-prep platform, presented directly by its founders.

🔗 Attend in person or online:

📍 On Campus Location: BYENG 209 (ASU Tempe)
💻 Online: https://asu.acm.org/attend
🗓 Date: Wednesday, November 5, 2025
⏰ Time: 6:30 PM – 8:00 PM MST
For more info and RSVP: https://asu.acm.org/rsvp

👥 Speakers:
– Abinash Mohanty, Staff Machine Learning Engineer at Meta
– Sachin Grover, Postdoctoral Researcher at ASU
– Neelesh Lalwani, Co-founder at WittyByte

💡 What you’ll learn:
– Understand how a typical ML Interview Loop is structured and what each stage is actually testing.
– See how to translate your projects into impact-focused resume bullets that get callbacks.
– Learn the key “do’s and don’ts” of crafting an ML resume that stands out to recruiters and hiring managers.
– Discover how WittyByte (https://www.wittybyte.ai/) can structure your practice with realistic prompts and feedback.
– Open Q&A with Speakers

Whether you’re targeting an MLE/AI/Data role in the next recruiting cycle or just training your first ML model, this session is for you. Join if you want clarity on how ML interviews really work, how to turn projects into impact-driven resume bullets, and how to structure your prep without burning out. You’ll leave with a clear, actionable plan to accelerate your path to an MLE role – plus a focused look at tools like WittyByte to streamline your practice.

School of Computing and Augmented Intelligence
Ira A. Fulton Schools of Engineering at Arizona State University
Arizona State University
ACM, Association for Computing Machinery

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