This role pays more than
98%
of similar roles. Most pay
$167,500–$235,750
— the shaded band above.
At the midpoint, this role pays about
$290k
versus about
$202k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
929 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 915 roles with salary data.
As a Senior Developer Technology Engineer - Simulation Performance, you will join the team to help partners and customers optimize demanding physics and robotics simulation workloads on NVIDIA platforms. You will be responsible for building rigorous benchmarks and performance models while profiling applications from system architecture through GPU kernels to identify limiting factors. Your daily work involves collaborating with developers to improve algorithms, data structures, memory layouts, and parallel execution. You will write and upstream production C++, CUDA, Python, and domain-specific GPU code into simulation platforms and reference implementations. The role requires expertise in mathematics, physics, numerical methods, geometry, or performance modeling. You will solve complex problems in robotics kinematics, reinforcement learning, and computational engineering while utilizing tools like Warp, Newton, PhysX, Isaac Sim, Isaac Lab, MuJoCo, and JAX to deliver reproducible improvements in simulation systems.
Develop performance strategies for physics, robotics simulation, and computational engineering workloads on NVIDIA GPUs.
Build rigorous benchmarks and performance models to identify limiting factors in customer applications.
Profile software from system architecture through GPU kernels to optimize execution.
Improve partner algorithms, data structures, memory layouts, and parallel execution patterns.
Write and upstream production C++, CUDA, Python, and domain-specific GPU code into simulation platforms.
Apply knowledge of reinforcement learning and robotic kinematics to solve technical challenges for partners.
Collaborate with internal engineering teams to resolve identified platform bottlenecks.
Create reusable reference implementations and documentation to ensure improvements last beyond single engagements.
What we're looking for
12+ years of relevant experience.
A degree in Computer Science, Applied Mathematics, Physics, Mechanical Engineering, Computational Science, or a related field (or equivalent experience).
Publicly inspectable stewardship of a consequential open-source engine, library, standard, benchmark, or software framework.
A record of delivering large, reproducible performance improvements in production numerical, simulation, graphics, CAE, robotics, or scientific-computing systems.
Expert modern C++ and GPU parallel-programming ability backed by strong foundations in mathematics, physics, numerical methods, geometry, or performance modeling.
Experience with physics engines, numerical solvers, geometry processing, differentiable simulation, or large-scale batched simulation (preferred).
Experience with CUDA, Warp, Newton, PhysX, Isaac Sim, Isaac Lab, MuJoCo, JAX, or comparable accelerated-computing systems (preferred).
A history of carrying technical work through integration, maintenance, documentation, and the success of other developers (preferred).
C++20
Python
gRPC
Nix
Entity-Component-System
Linux
pybind11
DIS Protocol
Systems Programming
Linear Algebra
Physics Modeling
Scientific Computing
C ABI
Distributed Simulation
C++
C
x86
ARM
GPU
Parallel Programming
Vectorization
CPU Intrinsics
Concurrent Data Structures
Spark
HPC
Accelerated Computing
Memory Subsystem
Data Analytics
Algorithms
Storage Systems
Networking