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 an Applied Mathematics Software Engineer, you will work at the intersection of applied mathematics, scientific computing, and low-level software. From day one, you will help shape how scientific workloads are expressed, executed, and optimized on the SPU. This role places strong emphasis on working close to the underlying computer architecture, understanding its intersection with low-level software, and influencing compiler behavior to achieve high performance. 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.
We are targeting a start date in August or September, aligning this role to contribute early to the development of our mathematical libraries and applications. This role is ideal for someone who enjoys moving fluidly between mathematics and low-level software, and who wants to help shape a new scientific computing platform from the ground up.
ResponsibilitiesPrototyping and implementing core numerical kernels and libraries for the SPU
Developing scientific applications and workloads on early SPU software stacks (e.g., seismic imaging, CFD, inverse problems, spectral methods)
Writing Python and C/C++ simulations or reference implementations of applied mathematics workloads to guide architecture decisions
Translating mathematical formulations into executable, performance-relevant software representations
Collaborating closely with hardware architects to evaluate algorithm–architecture tradeoffs
Working with compiler and runtime teams to ensure workloads map cleanly to the SPU programming model
Designing benchmarks, correctness tests, and performance models for scientific workloads
Iteratively refining workloads based on hardware evolution and performance insights
Strong foundation in applied numerical mathematics and scientific computing.
Strong proficiency in C, C++ and Python.
Comfort working close to hardware and writing performance-critical, low-level code.
Experience with scientific computing libraries (BLAS, LAPACK, FFTW, cuBLAS, cuFFT, etc.)
Experience with parallel or accelerator programming models (CUDA, OpenMP, MPI, SYCL, HIP)
Ability to reason about performance, memory hierarchies, and parallel execution
Familiarity with architecture-specific intrinsics, including warp-level operations, atomics, and synchronization primitives.
Experience working with low-level GPU assembly, such as NVIDIA SASS, or equivalent native accelerator instruction sets
Familiarity with performance analysis tools or modeling techniques (profilers, roofline models)
Exposure to compilers, runtimes, or code generation frameworks
Experience in applied scientific domains (physics, geophysics, CFD, climate, materials, finance)
Familiarity with low-level system software or drivers.
Excellent written and verbal communication skills.
Strong ability to work independently and collaboratively in a team.
Willingness to put in the hard work needed to bring our SPU to life.
Above all: zero 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!
Top Skills
What We Do
Vorticity Inc. is redefining how the world tackles complex scientific and engineering problems. We design and build the full technology stack — from custom silicon to cloud-accessible software platforms — to accelerate physics-based simulation and data-intensive computation across industries.
Our mission is to dramatically reduce the time and cost of scientific discovery by re-thinking every layer of the compute stack. We develop specialized processors and distributed systems optimized for solving large-scale partial differential equations (PDEs), enabling simulations that were once limited to supercomputers to run efficiently in the cloud.
Our technology is used in aerospace, energy, geophysics, and life sciences, helping teams design faster, explore deeper, and understand more — from mapping mineral resources to modeling hypersonic flight or molecular dynamics.
At Vorticity, we believe that innovation happens at the intersection of science, mathematics, and engineering. That’s why our team includes experts in computational physics, high-performance computing, and chip design, all working together to push the boundaries of what’s possible in simulation.
Our ultimate goal: to make world-class scientific computing accessible, scalable, and transformative — empowering researchers and engineers everywhere to move from ideas to breakthroughs faster than ever before.
Why Work With Us
Vorticity is a place for people who love to solve hard problems — the kind that shape the future of science and technology. We’re building a team-first culture where curiosity, rigor, and creativity thrive.
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