Web development has popularized the concept of a “full stack developer”—someone who is comfortable working on every part of an application, from the user-facing front-end to the server-side back-end and the database it connects to, with a holistic view of the entire web stack.

But what is the equivalent of “full stack” in the world of systems programming?

Redefining the “Stack”

To answer this, we need to first identify what constitutes the “stack” in systems programming:

  1. The Hardware & Firmware Layer: This represents the lowest level of the stack. It typically involves understanding how processors work, memory hierarchies, and how to write code that interfaces directly with hardware components such as microcontrollers common in embedded systems.

    • Key Skills: Writing bootloaders, firmware, and device drivers. Assembly language knowledge is often valuable.

  2. The OS Kernel Layer: This is the heart of the operating system, managing how programs access system resources like memory, CPU time, and hardware devices. This can range from complex, full-featured kernels like Linux down to minimal Real-Time Operating Systems (RTOS) built for embedded devices.

    • Key Skills: Developing or modifying the process scheduler, memory manager, file systems, and the networking stack.

  3. The System Libraries & API Layer: This layer provides the crucial interface between applications and the kernel, allowing programs to request system resources through well-defined APIs.

    • Key Skills: Building and maintaining standard libraries, creating system call interfaces, and designing application runtime environments.

  4. The Development Toolchain Layer: This encompasses the tools and infrastructure needed to build, deploy, and manage software systems.

    • Key Skills: Working on compilers and linkers, cross-compilation environments, and developing deployment infrastructure like virtualization or container platforms.

Where the Analogy Bends

In web development, the layers are more clearly defined and have well-established interfaces—frontend communicates with backend services through standardized protocols, backend services interact with databases through defined APIs, and each layer has distinct responsibilities. In systems programming, the boundaries between layers are often less clear and more interdependent.

The modern era has further complicated this landscape and expanded what “full stack” might mean. Today’s systems often span distributed infrastructure and cloud platforms, involving technologies like container orchestration, distributed databases, and high-performance networking protocols. This has given rise to “Full Stack Infrastructure Engineers” who work across the entire spectrum—from optimizing individual server kernels to designing how thousands of machines communicate and coordinate.

Similarly, the rise of AI has created roles like Machine Learning System Engineers who need systems knowledge spanning GPU programming, distributed training frameworks, model optimization, and deployment infrastructure. They bridge the gap between research and production, understanding both the low-level systems that power AI workloads and the high-level software stacks that make them efficient.

Conclusion

So, what is the full stack equivalent of systems programming?

The answer reveals a fundamental paradox: the “full stack” is both deeper and broader than any individual can truly master. Yet the closest equivalent isn’t about achieving complete mastery—it’s about developing a distinctive mindset that embraces curiosity across boundaries and comfort with interconnected complexity.

“Full stack” systems programmers often tend to see the computer as one interconnected system rather than separate layers. They might understand that a performance bottleneck could originate in hardware cache behavior, manifest in kernel scheduling, and require optimization at the application level. This cross-layer thinking, rather than encyclopedic knowledge, becomes their distinctive strength.

Today’s computing landscape—stretching from tiny embedded systems to massive AI clusters—means both deep specialists and boundary-crossing generalists have essential roles to play.