Staff Machine Learning Engineer at Apple
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Interested in software engineering, machine learning (ML), deep learning, Natural Language Processing (NLP), Artificial Intelligence (AI) and data science. Before working at Apple, I graduated with a degree in Computer Science from UC Berkeley in 2017. Feel free to contact me with relevant opportunities at badanidhrushil@gmail.com
University of California, Berkeley
Bachelor of Arts - BA, Computer Science
January 1, 2014 – January 1, 2017
Apple
Staff Machine Learning Engineer
February 1, 2018 – Present
San Francisco Bay Area
University of California, Berkeley
Instructor (Undergraduate Student Instructor) - Stat 134 Probability
August 1, 2017 – December 1, 2017
San Francisco Bay Area
Microsoft
Software Engineering Intern (Machine Learning, Data Mining, Backend)
May 1, 2017 – August 1, 2017
Greater Seattle Area
University of California, Berkeley
Undergraduate Research Assistant (Machine Learning, Multimedia)
August 1, 2016 – May 1, 2017
San Francisco Bay Area
Microsoft
Explorer Intern (Machine Learning, Backend, Android)
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Greater Seattle Area
The Genius Donkey
Software Engineering Intern
July 1, 2015 – August 1, 2015
Mumbai Metropolitan Region
Exchange4Talent
Software Engineering Intern
June 1, 2015 – August 1, 2015
Mumbai
Deep Learning for Music
January 1, 2017 – Present
Fixed-size vector representations of music signals for genre classification via an LSTM Autoencoder (as part of research with UC Berkeley & ICSI) for tasks such as live-song identification and genre classification
Cultural Fit Analysis
The candidate has a strong background in research and large-scale enterprise ML/AI development. While there are early internships involving Android app development and UI design, the primary career trajectory has been heavily backend and machine learning focused. The target role of 'Frontend Developer' represents a significant pivot from their recent experience. This indicates a potential mismatch in cultural fit for a dedicated frontend role, as their expertise lies in a different domain. The project diversity is high within ML/AI, but low for frontend-specific work.
Soft Skills & Operational Fit
The candidate's experience as an Instructor at UC Berkeley suggests strong communication and mentorship skills. Leading N+ engineers across M teams at Apple indicates leadership and project management capabilities. The detailed descriptions of complex ML projects imply strong problem-solving and analytical skills. However, there is no direct assessment of soft skills or operational fit for a Frontend Developer role.