Senior Solution Architect - Kernel & ML Performance (Hybrid/Remote)

EPAM Systems; Kraków; Hybrid; PLN 260,000 - 420,000; Full time

Benefits offered by this job

Hybrid by design

Remote within Poland

Relocation opportunities

English classes

Stock Purchase Plan

Health insurance

Job summary

EPAM Systems in Kraków seeks a Solution Architect (Kernel Optimization & ML Performance) to drive architecture for high‑performance ML workloads. You will work with ML researchers, compiler engineers and systems architects to deliver scalable, efficient solutions and set a strategic roadmap for kernel optimization.

You will guide benchmarking, autotuning, and performance profiling while ensuring security and maintainability across large‑scale AI workloads.

Qualifications

• Bachelor’s or Master’s degree in Computer Science or equivalent.
• 12+ years software engineering experience with 5+ years in architecture/leadership.
• Strong knowledge of ML frameworks and Core ML concepts.

Responsibilities

• Define and own architecture for performance-critical ML workloads.
• Create a strategic roadmap for kernel optimization and framework integration.
• Collaborate with ML researchers and engineers to capture requirements.
• Evaluate technologies and design patterns to optimize compute operations.
• Guide benchmarking, autotuning, and profiling frameworks.
• Advocate ML performance best practices across teams.
• Provide technical leadership to development teams.
• Ensure security, scalability, and maintainability in design.

Skills

Kernel optimization

ML performance

C++/Python

Architectural leadership

ML frameworks (JAX, PyTorch, TensorTF)

Compiler principles

Performance profiling

Education

Bachelor’s or Master’s degree in Computer Science

Tools

MLIR

OpenXLA

Job description

EPAM Systems in Kraków seeks a Solution Architect (Kernel Optimization & ML Performance) to drive architecture for high‑performance ML workloads. You will work with ML researchers, compiler engineers and systems architects to deliver scalable, efficient solutions and set a strategic roadmap for kernel optimization.

You will guide benchmarking, autotuning, and performance profiling while ensuring security and maintainability across large‑scale AI workloads.