Violinist's leap to machine learning on LinkedIn

Music and engineering may seem like almost opposite career paths. But for Javier Orman, the transition from a professional violinist to a machine learning engineer at LinkedIn was surprisingly natural.

Growing up in Montevideo, Uruguay, Orman excels in both music and mathematics, staying double in university subjects. However, music was his true passion, and after university he pursued a career as a professional violinist, helping him perform, teach, record and produce other artists.

However, amid the chaos of the Covid-19 pandemic, a conversation with alumni friends who gave up music for software development piqued his curiosity. After taking free online courses in Python and machine learning, he quickly became immersed in a fascinating new world of data and algorithms. It didn't take long to realize he wanted to create a career in that.

Machine learning algorithms were “almost magical” for Orman. “I was hooked on the methodology, the mathematics behind it.”

Double Genius

Orman's unusual career trajectory can be traced back to his childhood. His parents were both software engineers, and he grew up on computers around the house from an early age. But they were also a musical family. His mother enjoyed playing the piano and his father playing the trumpet.

Orman's path from music to machine learning shows how people with technical backgrounds can succeed with software.Desmond “Demoney” OWUSU/Instagram

His musical journey began at the age of four when he saw a class of about 100 children playing the violin as a group. Orman was captivated and immediately told his mother that he wanted to play the violin as well. By his teenage years he began to tour with the National Youth Orchestra in Uruguay and participated in music competitions. But at the same time, he discovered a natural aptitude for mathematics and entered the mathematics Olympics. But Orman says mathematics was essentially a hobby he sided with while he focused on his music career.

After completing two degrees in music and mathematics at the University of Charleston in South Carolina in 2006, Orman received his Masters in Music from the University of Michigan. By 2009 he had performed in an orchestra at Carnegie Hall in New York City and toured South America with chamber music groups.

Interested in pursuing a more creative path, Orman began composing music for short films, teaching music production, and eventually building a small studio to record and produce other artists. Over time, he created a sustainable music career by combining the patchwork of this creative project with violin teaching.

New direction

In early 2020, with Covid-19 overturning the world, Orman found himself reassessing his future and looking for new challenges. Near the start of the pandemic, he spoke to a graduate friend who recently made the transition from professional violinist to software engineering. She told him about programming languages ​​and what a career would look like, and out of curiosity he decided to take an online Python course. Shortly after he began exploring the world of machine learning.

“I started taking online courses and started looking for data on what I was interested in,” he says. For example, Orman has created an animated heatmap showing Covid-19 hospitalization rates for each state. “If I had created a cool plot and found a way to explore the data a little more, it actually became fun.”

Within six months, Orman realized this was something he wanted to pursue as a career. “I found myself having a hard time quitting to get a break from eating or falling asleep,” he says. “So I started taking it seriously.”

In April 2021, Orman prepared data at New York City-based Koios Medical to develop a cancer detection algorithm. But his big break came months later when he discovered LinkedIn's Reach Apprenticeship program. He applied in July as an apprentice in machine learning software engineering and began.

Learning Machine Learning

Orman is assigned to LinkedIn's feed AI team to develop recommended algorithms that determine which posts users will see. The system has multiple layers that gradually remove millions of potential posts to determine what is most interesting to a particular user.

In 2022, one year after the reach program, Orman was promoted to software engineer and is currently working on a model known as the “Second Pass Ranker,” the final layer of AI for the system. This determines which posts are most relevant to your users based on factors like their tendency to click or comment on similar types of posts.

Much of his job involves experimenting with new machine learning techniques or making small adjustments to the model and squeezing out additional performance. “It's a pretty complicated system,” Orman says. “This is also a very mature system, so we measure profits at a tenth or hundreds of percent.”

However, he enjoys the challenges and pushes to continue learning new things. It is something he believes in his background in music, which requires constant dedication and practice and should do well with him.

There is also a deep mathematical foundation to music, and Orman believes these connections have helped him in his new career. “These intersections run deep and are difficult to explain,” he says. “But they both feel like they're tickling my brain in a certain way.”

Orman also receives advice from others who take part in engineering from a non-technical background. Before diving into the core details, it focuses on developing instincts about how technology works. “Just spending your time and feeling how things work on an intuitive level, it all becomes easier,” he says. “And then you start practicing nuts and bolts.”

One day, he wants to marry his two major passions by working on recommended algorithms for music. In the meantime, he is pleased that they will play an individual and complementary role in his life. “I like taking breaks from work and playing Bach,” he says. “It feels like a good balance between the two.”