Vorticity

Vorticity is building the world’s first Scientific Processing Unit (SPU), a new class of silicon purpose-built to accelerate scientific computing beyond the limits of GPUs. We are designing tightly coupled software–hardware systems around applied mathematics workloads to deliver order-of-magnitude performance gains. Unlocking its full potential requires early, deep engagement from applied mathematics–driven software engineers who can translate real-world scientific workloads into executable models, kernels, libraries, and applications that inform both architecture and tooling decisions. As a Kernel Engineer, you will work at the intersection of applied mathematics, scientific computing, parallel programming, and low-level performance engineering. You will help shape how numerical kernels are implemented, optimized, and eventually mapped onto the SPU. Your work may include building early numerical kernels and libraries, developing prototype applications, and writing Python-based workload models and simulators, all to support and inform the evolving hardware and compiler stack. This requires both strong applied math fundamentals and deep low-level implementation ability. You should be comfortable moving from mathematical formulations to efficient kernels, reasoning about accuracy, stability, data movement, memory hierarchy, parallel execution, and compiler behavior along the way. This position is ideal for someone who combines strong scientific computing instincts with the low-level habits of a performance engineer. Responsibilities Prototyping and implementing core kernels and low-level numerical primitives for the SPU. Translating mathematical formulations into executable, performance-relevant kernel implementations. Analyzing and optimizing memory-access patterns, including coalescing, locality, shared memory usage, cache behavior, register pressure, and host-device data movement. Collaborating closely with hardware architects to evaluate algorithm–architecture tradeoffs around memory hierarchy, synchronization, vector/SIMT execution, instruction behavior, and parallel scheduling. Working with compiler and runtime teams to ensure kernels map cleanly to the SPU programming model. Designing microbenchmarks, correctness tests, numerical accuracy tests, and performance models, then iteratively refining kernels based on hardware evolution, compiler behavior, profiler output, and measured performance. Core Skills: Strong applied mathematics and scientific computing judgment, with the ability to understand numerical workloads deeply enough to implement them correctly and efficiently. Strong proficiency in C++ and CUDA, HIP, SYCL, or an equivalent accelerator programming model. Experience writing custom kernels, not just using existing frameworks or vendor libraries. Ability to translate mathematical formulations into low-level implementations while balancing accuracy, stability, precision, data movement, and performance. Deep understanding of GPU execution and memory hierarchy, including global memory, shared memory, registers, caches, coalescing, atomics, reductions, scans, warp-level execution, and occupancy. Experience using profiling and performance tools to identify bottlenecks, test hypotheses, and validate improvements. Ability to reason from profiler output to concrete code changes, rather than treating performance debugging as guesswork. Solid concurrency fundamentals, including race conditions, atomicity, synchronization, and thread/process execution behavior. Nice to Have Skills: Familiarity with performance analysis tools or modeling techniques (profilers, roofline models) Exposure to compilers, runtimes, or code generation frameworks Experience in applied scientific domains such as physics, geophysics, CFD, climate, materials, fusion, or finance. Experience with low-level GPU assembly or intermediate representations. Familiarity with low-level system software or drivers. Non-Technical Qualities: Excellent written

Vorticity is building the world’s first Scientific Processing Unit (SPU), a new class of silicon purpose-built to accelerate scientific computing beyond the limits of GPUs. We are designing tightly coupled software–hardware systems around applied mathematics workloads to deliver order-of-magnitude performance gains. Unlocking its full potential requires early, deep engagement from applied mathematics–driven software engineers who can translate real-world scientific workloads into executable models, kernels, libraries, and applications that inform both architecture and tooling decisions. As a Applied Math Libraries Engineer, you will work at the intersection of applied mathematics, scientific computing, and low-level software. From day one, you will help build the numerical software foundation of the SPU. This role is focused on building reusable mathematical primitives rather than full end-to-end scientific applications. The ideal candidate is excited to implement mathematics on a new architecture. You will work closely with hardware architects, compiler engineers, and runtime engineers to shape how numerical algorithms are expressed, executed, and optimized on the SPU. This position is ideal for someone who enjoys moving fluidly between applied math, numerical algorithms, and low-level software, and who wants to help build a new scientific computing platform from the ground up.   Responsibilities Prototype and implement core numerical linear algebra kernels and libraries for the SPU. Translate mathematical algorithms into executable, performance-relevant software. Write C, C++, and Python reference implementations to guide hardware, compiler, and runtime decisions. Design benchmarks, correctness tests, numerical accuracy tests, and performance models for numerical libraries and scientific workloads. 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. Iterate based on hardware evolution, compiler behavior, benchmark results, and performance insights. 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