Damilola Babalola

My ML Engineer interview at Qualcomm experience surprised me (in a positive manner) So, I was just taking my shot, and had little expectations of a call back but it did and somehow I panicked because I had to start my interview preparation. Here’s how it went: Round 1: Technical This was mostly ML-focused & small assignment with a classification dataset and performance explanation. Round 2: Coding + Systems Thinking Even as an ML role, DSA wasn’t ignored. Then came system-level questions: - How would you productionize a model? - What happens from model training to real-time inference? Round 3: Hiring Manager Round This was more conversational, and focused on: - How do I handle ambiguity in data? - How do I collaborate with cross-functional teams? - End-to-end explanation of one impactful project (metrics + challenges + results) Round 4: With top-level management Mostly culture-fit, curiosity, and future growth potential. And that’s it, I got selected as an ML Engineer at Qualcomm! Looking back, here’s what helped me most! - Having real projects I could speak about confidently - Practicing mock interviews (especially storytelling) - A resume that gave interviewers talking points I did all of this without quitting my job but what really helped me was mentorship from industry experts, so If you’re a working professional who wants to switch to data science, then don’t do it alone, you must check out Bosscoder Academy. Check them here - http://bit.ly/4kPAx92 They have helped 2200+ working professionals through structured curriculum, personal mentorship from professionals, real business case studies, placement support and much more. If you bring consistency, they bring direction. #qualcomm #collab #hiring #job

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🚀 Excited to share our NeurIPS acceptance Our paper “Explanation Driven Counterfactual Testing for Faithfulness in Vision Language Model Explanations” by Sihao Ding, Santosh V. and Aditi Ramadwar (Mercedes-Benz Research & Development North America, Inc.) has been accepted to the NeurIPS 2025 Regulatable ML Workshop, and we will be presenting our poster next month. 👉 EDCT provides a structured protocol for verifying VLM explanations and helps close the gap between a "model sounding correct" and a "model actually reasoning correctly" about the scene. Read the full paper here: https://lnkd.in/gpsYhTsm 💡 Why this work matters: Vision Language Models are starting to play a larger role in high stakes domains like autonomous driving, robotics and safety critical perception. These models often provide natural language explanations for their decisions, but those explanations can sound correct even when they do not reflect the model’s actual reasoning. This creates major reliability and safety challenges. 💡 What we introduce: We propose Explanation Driven Counterfactual Testing (EDCT), a method that treats the model’s own explanation as a testable hypothesis. We extract key visual concepts from the explanation, generate targeted counterfactual edits to the image and then re-query the VLM to evaluate whether both the answer and the explanation respond in a logically consistent way. This gives us a measurable Counterfactual Consistency Score (CCS) that captures how faithful and causally aligned a model’s explanation truly is. 💡 Why this is important for autonomous safety: As teams explore VLM based end to end driving systems, auditing the reasoning behind a model’s decisions becomes essential. For example, if a system claims it slowed down because a cyclist signaled left, we should be able to minimally change that signal and verify that the model’s answer and explanation change accordingly. If not, there is a safety gap. Looking forward to presenting this work at NeurIPS and connecting with others working on regulatable, multimodal models and autonomous safety. #NeurIPS2025 #RegulatableML #VisionLanguageModels #VLMs #AIExplainability #SafetyCriticalAI #AutonomousVehicles

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Very good discussion on the history of positional embeddings. One of our main goals as engineers is to understand (1) how to make things better / improve architectures and (2) understand how the architecture we are using actually maps to reality (and how it can actually hinder performance). If you are unfamiliar with Sinusoidal positional encodings or DroPE / RoPE, I'd highly recommend checking this out! #AI #GenAI #LLM #PositionalEncodings #ComputerScience #Engineering #DataScience #MachineLearning #DeepLearning #ML #DL

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