Remote Senior High-Performance AI Engineer - GPU & Systems

Ladders; United States; Remote; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity options

Professional growth opportunities

Cutting-edge AI tools

Job summary

Client, a leader in Telecommunications & Hardware, seeks a Senior High Performance AI Engineer to design and optimize AI systems for accelerated computing environments. You will collaborate with software, hardware, and algorithm teams to boost performance, reliability, and quality across the AI stack.

The role suits engineers who enjoy tackling cross-stack challenges spanning model orchestration, inference, and system-level optimization, with emphasis on scalable, reproducible evaluation and

Qualifications

• 8+ years of AI systems development, incl. foundational models and orchestration frameworks.
• Strong programming in C/C++ and Python with solid software engineering fundamentals.
• Experience in GPU programming and performance optimization.

Responsibilities

• Design, build, and optimize agentic AI systems for accelerated computing environments.
• Co-design system solutions with software, hardware, and algorithm teams.
• Develop reproducible evaluation frameworks for performance and quality.
• Collaborate across the AI stack, from hardware to inference and serving.
• Influence and adopt new capabilities as they become available.

Skills

C/C++

Python

GPU programming

Software engineering

Deep learning

Education

Bachelor's degree in CS/EE

MS or PhD preferred

Tools

CUDA

Orchestration frameworks

Job description

Client, a leader in Telecommunications & Hardware, seeks a Senior High Performance AI Engineer to design and optimize AI systems for accelerated computing environments. You will collaborate with software, hardware, and algorithm teams to boost performance, reliability, and quality across the AI stack.

The role suits engineers who enjoy tackling cross-stack challenges spanning model orchestration, inference, and system-level optimization, with emphasis on scalable, reproducible evaluation and