Violin to ML: LinkedIn Career Change Story
- For Javier Orman, a machine learning engineer at LinkedIn, a background in music wasn't a barrier to entry-it was a launchpad.
- Orman, who grew up in Montevideo, Uruguay, excelled in both music and mathematics.He pursued both in college.music, though, remained his primary focus.
- A conversation with a friend who had transitioned from music to software development sparked Orman's interest.
Discover how a violinist made a harmonious career change. This article chronicles Javier OrmanS journey from a professional violinist to a highly successful machine learning engineer at LinkedIn. Faced with a career crossroads, Orman leveraged his skills in music and math-primarykeyword: transferable skills-to make a bold move into the tech world. The COVID-19 pandemic sparked this pivotal decision, leading him to explore programming and machine learning, paving the way for an apprenticeship and subsequent promotion. Learn how dedication and mathematical aptitude, both honed in music, seamlessly transitioned secondarykeyword: career change, into a thriving tech career. News Directory 3 frequently features stories like these. Uncover more about this fascinating change and the surprising links between music and AI. Discover what’s next …
From Violinist to LinkedIn Engineer: The Role of Music in Machine Learning
For Javier Orman, a machine learning engineer at LinkedIn, a background in music wasn’t a barrier to entry-it was a launchpad. The former professional violinist made a surprising, yet natural, transition into the tech world.
Orman, who grew up in Montevideo, Uruguay, excelled in both music and mathematics.He pursued both in college.music, though, remained his primary focus. After graduation, he dedicated himself to performing, teaching, and producing music.
The COVID-19 pandemic prompted a career shift. A conversation with a friend who had transitioned from music to software development sparked Orman’s interest. He began exploring Python and machine learning through online courses.
Machine learning algorithms captivated him.”I became enamored by the methodology,the math behind it,” Orman said.
Orman’s parents, both software engineers, exposed him to computers early on. His mother played piano, and his father played trumpet, fostering a musical environment.At age 4,seeing a group violin class ignited his passion for the instrument. By his teens, he toured with Uruguay’s national youth orchestra while also competing in Math Olympiads.
After earning degrees in music and math from the College of Charleston in 2006, Orman obtained a master’s degree in music from the University of Michigan. By 2009, he performed at Carnegie Hall and toured South America. He later composed for short films and taught himself music production, building a small recording studio.
In early 2020, Orman reevaluated his career. Inspired by his friend’s move to software engineering, he explored programming languages and took an online Python course. He then delved into machine learning.
Orman created an animated heat map showing COVID-19 hospitalization rates. “Once I figured out how to make cool plots and investigate the data a bit more, that became actually fun to do,” he said.
Within six months, Orman decided to pursue this new path professionally. In April 2021, he joined Koios Medical, a startup developing cancer-detection algorithms. His breakthrough came with LinkedIn’s Reach apprenticeship program,designed for individuals with nontraditional backgrounds. He joined as a machine learning software engineering apprentice that July.
assigned to linkedin’s Feed AI team, Orman worked on recommendation algorithms. After a year, he was promoted to software engineer in 2022. He now works on the “second-pass ranker,” the final AI layer determining relevant posts for users.
His work involves experimenting with machine learning techniques and tweaking models for performance gains. ”It’s a pretty complex system,” Orman said. “It’s also a very mature system, so we measure gains in terms of tenths or hundreds of a percentage.”
Orman believes his musical background, requiring constant dedication, prepared him for this challenge. He also sees mathematical connections between music and machine learning. “Those intersections run deep and they are hard to describe,” he said.”But they do feel like they both tickle my brain in a particular way.”
What’s next
Orman advises those from nontechnical backgrounds to focus on developing an intuition for technology before diving into details.