Data science jobs requiring C/C++

Why C/C++ Jobs Are in High Demand in 2026

C and C++ together represent the foundational systems programming languages of the data and AI infrastructure stack in 2026. While they are distinct languages — C for low-level systems programming, C++ for object-oriented and performance-critical applications — job postings often list "C/C++" as a combined requirement, reflecting the expectation that engineers working at the systems level should be comfortable in both. The most performance-critical components of the data ecosystem — ML framework kernels, database engines, numerical libraries — are written in C or C++.

In the ML space, C++ powers the internals of PyTorch (ATen tensor library, autograd engine), TensorFlow (XLA compiler, custom op kernels), ONNX Runtime for cross-platform inference, and TensorRT for GPU-optimized inference. Engineers writing custom PyTorch extensions, implementing novel attention mechanisms as CUDA kernels, or building inference engines for custom hardware work primarily in C++. The performance gap between Python and C++ — often 100x or more for compute-intensive tasks — makes C++ the inevitable choice for production inference serving where latency is measured in milliseconds.

In the database space, virtually all high-performance databases (PostgreSQL, ClickHouse, DuckDB, RocksDB) are written in C or C++. Data engineers working on database internals, building custom storage engines, or contributing to open-source analytical databases need strong C++ skills. The combination of C/C++ with CUDA for GPU programming, Python for the ML framework layer, and systems design knowledge for distributed systems is a rare and highly compensated skill profile in 2026.