Applied AI @ Google X | PhD Astrophysics
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Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Extensive experience building ML systems that learn faint signals from a sea of noise. Spent many years working with remote-sensing data in the space industry, as a tech lead and managing teams. Now I work on memory scaling harnesses for deep agents, applied to specialized knowledge work.
University of Florida
Doctor of Philosophy (PhD), Astronomy and Astrophysics
January 1, 2009 – January 1, 2014
Universidad de Sevilla
Bachelor of Science (BS), Theoretical and Mathematical Physics
January 1, 2003 – January 1, 2009
X, the moonshot factory
Staff Research Engineer
February 1, 2025 – Present
Mountain View, California, United States · On-site
Planet
ML Software Engineer
August 1, 2023 – February 1, 2025
Planet
Senior ML Engineering Manager
February 1, 2022 – July 1, 2023
Planet
ML Engineering Manager
August 1, 2018 – March 1, 2022
Planet
Senior Machine Learning Engineer
April 1, 2017 – August 1, 2018
Capital One
Data Scientist
October 1, 2014 – March 1, 2017
San Francisco Bay Area
Insight Data Science
Fellow
June 1, 2014 – August 1, 2014
Palo Alto, CA
University of Florida
PhD - Astrophysics
August 1, 2009 – March 1, 2014
Geansolar
Co-founder of GeanSolar
January 1, 2004 – January 1, 2008
Chiclana, Spain
Cultural Fit Analysis
The candidate's diverse experience, ranging from a startup co-founder to a Staff Research Engineer at Google[x] and various ML roles at Planet, indicates a strong cultural fit for innovative and challenging environments. Their background in astrophysics and subsequent transition into applied ML demonstrates intellectual curiosity and a problem-solving mindset. The blend of individual contributor and leadership roles suggests flexibility and a collaborative approach, aligning well with dynamic team structures. The focus on impactful projects (e.g., fraud detection, environmental monitoring) suggests a drive for meaningful contributions.
Soft Skills & Operational Fit
The candidate's career progression from individual contributor to manager and back to a research engineer role suggests adaptability and a strong drive for technical depth. Experience in leading teams indicates leadership and collaboration skills. The co-founding of a startup demonstrates entrepreneurial spirit and business acumen. The transition from astrophysics to data science and then ML engineering shows a strong capacity for learning and applying complex concepts to real-world problems.