2025
ML Engineering at Apple @ 19
Landed ML Engineering at Apple @ 20 years old
Machine Learning
FAANG
Know More
Driven by curiosity and a love for building under pressure, every project I take on is a step toward scaling ideas into reality. I’ve learned that the most valuable skill is turning constraints into momentum.
Best Rated Journey: From Hackathons To Apple And Meta
Secured an engineering role at Apple as 1 of only 5 students from Howard University, marking an early breakthrough into one of the most selective teams in Silicon Valley.
Context
WTE
I joined the Wireless Technologies and Ecosystems team, one of the most selective orgs inside Apple. Being one of only a handful from Howard Engineering made the pressure real—but also gave the work more meaning. It felt like stepping into the deep end of a pool: sink or swim. I chose to swim.
Build
Shipping Pipelines
Developed ML pipelines that used telemetry data to predict carrier configurations across iOS devices, cutting manual triage by over a third. Integrated the system into CI/CD so it worked across iPhones, iPads, and watches automatically. This meant every release had intelligence baked in, reducing friction for millions of users.
Takeaway
Raising the Bar
Learned that scale is more than code but more about making systems invisible, reliable, and trusted. That’s the level Apple operates at, and it pushed me to raise my bar. I left with a sharper sense of discipline and a clearer understanding of what production-grade engineering truly means.
More Works
(GQ® — 02)
©2024
2025
ML Engineering at Apple @ 19
Landed ML Engineering at Apple @ 20 years old
Machine Learning
FAANG
Know More
Driven by curiosity and a love for building under pressure, every project I take on is a step toward scaling ideas into reality. I’ve learned that the most valuable skill is turning constraints into momentum.
Best Rated Journey: From Hackathons To Apple And Meta
Secured an engineering role at Apple as 1 of only 5 students from Howard University, marking an early breakthrough into one of the most selective teams in Silicon Valley.
Context
WTE
I joined the Wireless Technologies and Ecosystems team, one of the most selective orgs inside Apple. Being one of only a handful from Howard Engineering made the pressure real—but also gave the work more meaning. It felt like stepping into the deep end of a pool: sink or swim. I chose to swim.
Build
Shipping Pipelines
Developed ML pipelines that used telemetry data to predict carrier configurations across iOS devices, cutting manual triage by over a third. Integrated the system into CI/CD so it worked across iPhones, iPads, and watches automatically. This meant every release had intelligence baked in, reducing friction for millions of users.
Takeaway
Raising the Bar
Learned that scale is more than code but more about making systems invisible, reliable, and trusted. That’s the level Apple operates at, and it pushed me to raise my bar. I left with a sharper sense of discipline and a clearer understanding of what production-grade engineering truly means.
More Works
(GQ® — 02)
©2024
2025
ML Engineering at Apple @ 19
Landed ML Engineering at Apple @ 20 years old
Machine Learning
FAANG
Know More
Driven by curiosity and a love for building under pressure, every project I take on is a step toward scaling ideas into reality. I’ve learned that the most valuable skill is turning constraints into momentum.
Best Rated Journey: From Hackathons To Apple And Meta
Secured an engineering role at Apple as 1 of only 5 students from Howard University, marking an early breakthrough into one of the most selective teams in Silicon Valley.
Context
WTE
I joined the Wireless Technologies and Ecosystems team, one of the most selective orgs inside Apple. Being one of only a handful from Howard Engineering made the pressure real—but also gave the work more meaning. It felt like stepping into the deep end of a pool: sink or swim. I chose to swim.
Build
Shipping Pipelines
Developed ML pipelines that used telemetry data to predict carrier configurations across iOS devices, cutting manual triage by over a third. Integrated the system into CI/CD so it worked across iPhones, iPads, and watches automatically. This meant every release had intelligence baked in, reducing friction for millions of users.
Takeaway
Raising the Bar
Learned that scale is more than code but more about making systems invisible, reliable, and trusted. That’s the level Apple operates at, and it pushed me to raise my bar. I left with a sharper sense of discipline and a clearer understanding of what production-grade engineering truly means.
More Works
©2024