Applied Math Libraries Engineer (SPU)

Collaborate with hardware architects, compiler engineers, and runtime teams to evaluate algorithm–architecture tradeoffs and ensure numerical primitives map cleanly to the SPU programming model.

Core Skills:

• Strong foundation in applied mathematics, numerical linear algebra, and scientific computing, with the ability to turn mathematical ideas into correct and efficient software.

• Strong proficiency in C, C++, and Python.

• Comfort working close to hardware and writing performance-critical, low-level code.

• Experience implementing numerical algorithms yourself, rather than only using existing libraries.

• Ability to reason about memory layouts, cache behavior, bandwidth, arithmetic intensity, and parallel execution.

• Experience with parallel or accelerator programming models such as CUDA, OpenMP, MPI, SYCL, HIP, or similar.

• Solid understanding of concurrency fundamentals, including race conditions, atomics, synchronization, and thread/process behavior.

• Experience working with low-level GPU assembly, such as NVIDIA SASS, or equivalent native accelerator instruction sets.

Nice to Have Skills:

• Familiarity with numerical computing libraries such as BLAS, LAPACK, FFTW, Eigen, SuiteSparse, PETSc, cuBLAS, cuSOLVER, cuSPARSE, cuFFT, or similar.

• Experience building numerical libraries, solvers, scientific computing frameworks, or HPC infrastructure.

• Familiarity with performance analysis tools or modeling techniques, including profilers, roofline models, hardware counters, or analytical performance models.

• Exposure to compilers, runtimes, code generation frameworks, or domain-specific languages for numerical computing.

• Experience applying numerical methods in scientific domains such as physics, geophysics, CFD, climate, materials, fusion, or finance.

Non-Technical Qualities:

• Excellent written and verbal communication skills

• Strong ability to work independently and collaboratively in a team.

• Comfort operating in an early-stage environment where the hardware, compiler, and software stack are evolving together.

• Willingness to put in the hard work needed to bring the SPU to life.

• Above all: low ego.

As passionate scientists and engineers, we are well aware of the plethora of critical problems in the world that cannot be solved because humanity simply does not have enough computing power. To address this, Vorticity is developing a radically new silicon chip architecture and system to dramatically accelerate scientific computing problems.

Vorticity’s mission is to expand human ingenuity. To do that we are building a team of exceptional people to work together on big problems. Join us!

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