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Maggie vonEbers
Mid-level Research Engineer specializing in machine learning and computational neuroscience
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About
Master’s-level ML researcher with hands-on embodied/edge deployment experience: built a Google Glass motion-tracking system at Sandia using MobileNetV1 + LSTM trained in TensorFlow and deployed via TensorFlow Lite. Has reimplemented transformer-based research for a thesis and demonstrated strong judgment adapting quickly when upstream assumptions changed, and stays current through active reading groups and a JEPA collaboration.
Experience
Education
Key Strengths
- Built and deployed a lightweight motion-tracking pipeline (MobileNetV1 + small LSTM) to TensorFlow Lite for Google Glass
- Strong grasp of embodied AI production constraints (edge cases, temporal coherence, efficiency vs intelligence tradeoffs)
- Pragmatic approach to productionizing ML: wrap models in reliable systems, start simple, add testing, monitor logs/metrics for distribution shift
- High-judgment decision-making under ambiguity; successfully pivoted thesis approach after upstream paper/code changes under a 4-week deadline
- Active engagement with cutting-edge research via reading/debate groups and external collaboration (JEPA, Dreamer-style models)
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