- Senior Graphics Compute & Kernel Optimization Engineer
- Experience: 8–12 Years
- Education: B.Tech / M.Tech / MS / Ph.D. in Computer Science, Electrical Engineering, Computer Graphics, or a related discipline.
- Role Overview
- We are looking for an experienced Graphics Compute Engineer with strong expertise in low-level programming, parallel computing, compiler technologies, and performance optimization.
- The role involves adapting and optimizing advanced graphics, neural rendering, image processing, and scientific visualization workloads for high-performance, compute-oriented accelerator architectures. The engineer will be responsible for developing compute-based solutions to replicate graphics functionalities that may not be directly supported by the underlying hardware.
- The ideal candidate should have hands-on experience in C/C , GPU programming, compiler frameworks, memory optimization, and neural graphics technologies.

- Key Responsibilities
- 1. Graphics & Neural Computing Development
- Analyze and adapt advanced graphics workloads, including neural rendering, NeRF, 3D Gaussian Splatting, image reconstruction, denoising, and scientific visualization.
- Develop and optimize compute kernels for ray marching, volume rendering, image processing, and neural network-based graphics applications.
- Implement graphics-related workloads on parallel compute architectures using available software development kits and low-level programming frameworks.
- Explore alternative compute-based approaches to implement graphics functionalities that are not natively supported by the hardware.
- 2. Low-Level Programming & Compiler Optimization
- Develop, port,



and optimize high-performance kernels using C/C and GPU programming frameworks.
- Collaborate with compiler engineering teams to improve architecture-specific code generation using LLVM and MLIR.
- Optimize memory access, data movement, synchronization, and parallel execution across many-core processor architectures.
- Work on SIMD/MIMD execution models and improve workload distribution and computational efficiency.
- Develop software-based alternatives for traditional graphics pipeline operations such as rasterization, texture sampling, and ray tracing.
- 3. Performance Engineering & Benchmarking
- Design and execute performance benchmarks for graphics, rendering, and visualization workloads.
- Evaluate compute performance against established technologies and frameworks such as CUDA, OptiX, and Vulkan Compute.
- Identify performance bottlenecks related to memory bandwidth, compute utilization, synchronization, and kernel execution.
- Analyze hardware limitations and recommend optimization strategies to improve overall workload efficiency.
- Assess the feasibility, development effort, and performance trade-offs involved in implementing graphics functionalities through compute-based approaches.
- 4. Cross-Functional Collaboration




- Work closely with compiler, runtime, architecture, and hardware engineering teams to improve accelerator performance.
- Support the development and integration of optimized compute libraries and runtime components.
- Evaluate emerging neural graphics techniques and their applicability to next-generation compute architectures.
- Required Technical Skills
- Strong programming expertise in C and C .
- Hands-on experience with GPU programming frameworks such as CUDA, OpenCL, or Vulkan Compute.
- Strong understanding of parallel computing, multi-core and many-core processor architectures.
- Experience with compiler technologies such as LLVM and MLIR.
- Good knowledge of graphics pipeline concepts, including rasterization, ray tracing, texture sampling, and framebuffer operations.
- Experience in low-level kernel development and performance optimization.
- Robust understanding of memory hierarchy, memory management, synchronization, and parallel execution.
- Exposure to neural rendering and graphics technologies such as NeRF, Gaussian Splatting, neural denoising, and super-resolution.
- Preferred Qualifications
- Experience adapting or porting graphics and visualization applications to non-traditional GPU or compute accelerator platforms.
- Exposure to MIMD-based architectures and architecture-specific code optimization.
- Experience with scientific visualization, image reconstruction, rendering engines, or neural graphics pipelines.
- Ability to evaluate hardware capabilities and develop software-driven alternatives for unsupported graphics operations.

📌 Graphics Compute & Kernel Optimization Engineer (Bangalore Urban)
🏢 Saaki Argus And Averil Consulting
📍 Bangalore Urban