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In this episode, we talked with Aishwarya Jadhav, a machine learning engineer whose career has spanned Morgan Stanley, Tesla, and now Waymo. Aishwarya shares her journey from big data in finance to applied AI in self-driving, gesture understanding, and computer vision. She discusses building an AI guide dog for the visually impaired, contributing to malaria mapping in Africa, and the challenges of deploying safe autonomous systems. We also explore the intersection of computer vision, NLP, and LLMs, and what it takes to break into the self-driving AI industry.TIMECODES00:51 Aishwarya’s career journey from finance to self-driving AI05:45 Building AI guide dog for the visually impaired12:03 Exploring LiDAR, radar, and Tesla’s camera-based approach16:24 Trust, regulation, and challenges in self-driving adoption19:39 Waymo, ride-hailing, and gesture recognition for traffic control24:18 Malaria mapping in Africa and AI for social good29:40 Deployment, safety, and testing in self-driving systems37:00 Transition from NLP to computer vision and deep learning43:37 Reinforcement learning, robotics, and self-driving constraints51:28 Testing processes, evaluations, and staged rollouts for autonomous driving52:53 Can multimodal LLMs be applied to self-driving?55:33 How to get started in self-driving AI careersConnect with Aishwarya- Linkedin - https://www.linkedin.com/in/aishwaryajadhav8/Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

​Aishwarya Jadhav has worked at the forefront of applied AI: from large-scale recommender systems in finance to malaria mapping in Africa, and from self-driving systems at Tesla and Waymo. With a background in multimodal deep learning and computer vision from Carnegie Mellon University, she now leads efforts in gesture understanding and pedestrian semantics at Waymo.

​In this conversation, Aishwarya shares what it really takes to bring AI from research to production in safety-critical environments. We’ll discuss the trade-offs between accuracy and performance, how to evaluate perception models for deployment, and what lessons from AI for social good can teach big-tech engineering teams. ​ ​We plan to cover:

• ​What it takes to move perception and multimodal models into production
• ​How to evaluate and validate safety-critical AI systems
• ​Where multimodal AI makes a real difference, and where it adds complexity
• ​Lessons from malaria vector mapping for low-resource, high-impact applications
• ​Skills and habits engineers need to thrive in applied AI roles

​ ​About the speaker

​Aishwarya Jadhav is a Machine Learning Engineer at Waymo, where she leads projects on gesture understanding and pedestrian semantics. She previously worked at Tesla Autopilot AI on multimodal LLMs and video understanding, and at Morgan Stanley on large-scale recommendation systems. Aishwarya holds a Master’s degree in Computer Science from Carnegie Mellon University, where she focused on multimodal deep learning and text information extraction. She has also contributed to social impact projects, including malaria vector mapping in Africa, which helped reduce mosquito populations by 60% at minimal cost.

​Join our slack: https://datatalks.club/slack.html

​Aishwarya Jadhav has worked at the forefront of applied AI: from large-scale recommender systems in finance to malaria mapping in Africa, and from self-driving systems at Tesla and Waymo. With a background in multimodal deep learning and computer vision from Carnegie Mellon University, she now leads efforts in gesture understanding and pedestrian semantics at Waymo.

​In this conversation, Aishwarya shares what it really takes to bring AI from research to production in safety-critical environments. We’ll discuss the trade-offs between accuracy and performance, how to evaluate perception models for deployment, and what lessons from AI for social good can teach big-tech engineering teams. ​ ​We plan to cover:

• ​What it takes to move perception and multimodal models into production
• ​How to evaluate and validate safety-critical AI systems
• ​Where multimodal AI makes a real difference, and where it adds complexity
• ​Lessons from malaria vector mapping for low-resource, high-impact applications
• ​Skills and habits engineers need to thrive in applied AI roles

​ ​About the speaker

​Aishwarya Jadhav is a Machine Learning Engineer at Waymo, where she leads projects on gesture understanding and pedestrian semantics. She previously worked at Tesla Autopilot AI on multimodal LLMs and video understanding, and at Morgan Stanley on large-scale recommendation systems. Aishwarya holds a Master’s degree in Computer Science from Carnegie Mellon University, where she focused on multimodal deep learning and text information extraction. She has also contributed to social impact projects, including malaria vector mapping in Africa, which helped reduce mosquito populations by 60% at minimal cost.

​Join our slack: https://datatalks.club/slack.html