Senior Engineer, Machine Learning Application Developer

Jobtailor; San Jose (CA); On-site; USD 140,000 - 200,000; Full time

Job summary

Jobtailor in San Jose seeks an engineer to develop neural rendering and ML software enabling efficient AI workloads on premium mobile GPUs. You will optimize performance and collaborate with GPU architects and hardware teams to translate insights into software improvements.

The role requires strong CPU/GPU programming skills, experience with Vulkan/OpenGL/OpenCL, and ML frameworks like PyTorch/TensorFlow. You will work on diverse ML workloads and strive for efficient, scalable GPU solutions.

Qualifications

• Bachelor's degree or higher in CS/CE or related field.
• 3+ years of relevant experience OR 2+ years with a Master’s/PhD.
• Proficiency in C, C++, Python and API-level GPU programming.

Responsibilities

• Develop neural rendering applications and ML software for efficient AI workloads on premium mobile GPUs.
• Bridge ML workloads with GPU hardware capabilities and optimize performance.
• Collaborate with GPU architects, software engineers and hardware teams.
• Analyze performance and bottlenecks to improve efficiency and scalability across ML workloads.
• Leverage low-level analysis including assembly when needed to maximize GPU utilization.
• Stay current with ML, neural rendering and GPU technology advancements.

Skills

C

C++

Python

Vulkan

OpenGL

OpenCL

PyTorch

TensorFlow

GPU architecture

Performance profiling

Neural rendering

Assembly-level analysis

Problem solving

Communication

Education

Bachelor's degree in Computer Science/Engineering

Master’s degree in Computer Science/Engineering

Ph.D.

Tools

Vulkan

OpenGL

OpenCL

PyTorch

TensorFlow

Job description

Responsibilities

• Develop neural rendering applications and machine learning (ML) software that enable efficient execution of AI workloads on Samsung’s premium mobile GPUs.
• Contribute to the development of software solutions that bridge machine learning workloads and GPU hardware capabilities.
• Optimize performance, efficiency, and resource utilization to support next-generation intelligent computing experiences.
• Analyze software performance and hardware resource utilization to identify bottlenecks and optimize application performance, efficiency, and scalability across a variety of ML workloads.
• Collaborate with GPU architects, software engineers, and hardware teams to understand underlying hardware constraints and translate performance insights into optimized software solutions.
• Leverage low-level performance analysis techniques, including assembly-level investigation when needed, to improve execution efficiency and maximize GPU utilization.
• Take initiatives on moderate-to-complex projects and advance best practices and methodologies by staying current with the latest advancements in machine learning, neural rendering, and GPU technologies.

Requirements

• 3+ years of experience with a Bachelor's Degree in Computer Science, Computer Engineering, or comparable field, or 2+ years of experience with a Master’s Degree, or Ph.D.
• Strong programming skills in C, C++, and Python.
• Proficiency with API-level programming using Vulkan, OpenGL, OpenCL, and machine learning frameworks such as PyTorch and TensorFlow.
• Understanding of GPU hardware architecture and experience with low-level performance profiling, analysis, and optimization.
• Hands-on experience developing neural rendering applications at the API level.
• Working knowledge of machine learning operators and workloads, including GEMM, convolution, activations, and related computational kernels.
• Ability to analyze hardware resource constraints and bottlenecks and develop software optimizations that improve performance and efficiency.
• Working knowledge of assembly-level analysis, debugging, or optimization is preferred.
• Strong analytical and problem-solving skills, with the ability to identify bottlenecks and propose data-driven solutions.
• Excellent communication and collaboration skills, with the ability to navigate ambiguity in a fast-paced, global team environment.