Research Communications Lead, Physical AI

<p style="min-height:1.5em"><strong>About us</strong></p><p style="min-height:1.5em">We're solving AI's hardest problem: alignment.</p><p style="min-height:1.5em">Encord is the data layer for Physical AI and enterprise, serving as core infrastructure for 300+ of the world's leading AI teams, including Woven by Toyota, Hitachi, Skydio, AbbVie and Dyna, Walden and Agility Robotics. From autonomous vehicles and robotics to sports and healthcare, we're solving AI's hardest problem: turning complex, real-world data into production-ready intelligent systems.</p><p style="min-height:1.5em">We're a team of founders, scientists, quants, researchers, and former Olympians, who’ve helped build some of the most successful companies in Silicon Valley, and alumni of MIT, Stanford, UC Berkeley, Oxford, Cambridge, and Imperial. We’ve raised $110M from legendary technology investors, early backers of companies like Databricks, ElevenLabs and Vercel, and we’re growing at an extraordinary pace.</p><p style="min-height:1.5em">We're looking for exceptional people who want to help build a category-defining company, operating with high urgency and taking real ownership to help shape the future of AI.</p><p style="min-height:1.5em"></p><p style="min-height:1.5em"><strong>The role</strong></p><p style="min-height:1.5em">We recently released <a target="_blank" rel="noopener noreferrer nofollow" href="https://encord.com/data-labs/">Encord Data Labs</a>: our research arm through which we'll be sharing some of the lessons learned from our internal research on how data actually moves model performance in the physical world.</p><p style="min-height:1.5em">We're looking for our first Research Communications Lead, to build and drive our Data Labs research and publishing motion. You won't just be on the author list, you'll be identifying interesting problems and topics to explore, and working with the rest of our ML team to shape their results into research that helps progress our field.</p><p style="min-height:1.5em">This is a front-row seat to some of the most important technical challenges in physical AI, with a direct line to our research team and co-founders, as well as chance to explore the end-to-end journey of building autonomous systems and getting them to work in the real world.</p><p style="min-height:1.5em"></p><p style="min-height:1.5em"><strong>A taste of what Data Labs has worked on and published in its first months:</strong></p><ul style="min-height:1.5em"><li><p style="min-height:1.5em"><a target="_blank" rel="noopener noreferrer nofollow" href="https://encord.com/data-labs/introducing-emg-1/"><strong>EMG-1</strong></a><strong>:</strong> we released the largest open dataset of wrist EMG synced with egocentric video: 307 hours of real work captured on head and wrist cameras, plus a 10.4-hour lab set with optical mocap, over 30x larger than the next public dataset of its kind</p></li><li><p style="min-height:1.5em"><a target="_blank" rel="noopener noreferrer nofollow" href="https://encord.com/data-labs/introducing-emg-1/"><strong>EMG + vision fusion</strong></a><strong>:</strong> by adding muscle signals to camera-based hand tracking, we were able to reduce median fingertip error by ~25%, and ~35% when hands were briefly occluded</p></li><li><p style="min-height:1.5em"><a target="_blank" rel="noopener noreferrer nofollow" href="https://encord.com/dat