AI Performance Engineer: Life Sciences & GPU

AMD

Finland

Hybrid

EUR 70,000 - 110,000

Full time

7 days ago

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Job summary

AMD is seeking an AI Performance Engineer in Life Sciences to develop and optimize foundation models and agentic AI systems for drug discovery. You will join a multidisciplinary team of ML researchers, chemists, and software engineers to deliver production-ready AI solutions that have real-world therapeutic impact.

Responsibilities include profiling, kernel and backend optimizations, and collaboration with life sciences AI developers across Finland and beyond.

Qualifications

Master’s degree, PhD, or equivalent practical experience in Computer Science, Computer Engineering, Computational Science, Applied Mathematics, Cheminformatics, Bioinformatics, or a related field.
Experience delivering ML, simulation, or HPC solutions.
Experience building ML and simulation applications and pipelines.
Strong understanding of deep learning architectures including transformers, diffusion models, and language models.
Proficiency in Python and production-level C++.
Experience with PyTorch; familiarity with JAX is beneficial.
Software engineering skills across prototyping, debugging, profiling, optimization, and maintainable code.
Familiarity with AI-assisted development tools such as Claude Code or Cursor.
Experience with performance profiling tools such as rocprof, PyTorch Profiler, or Nsight.
Experience with framework-level optimization including compilation techniques and backends such as Inductor or TensorRT.
Experience profiling and optimizing GPU workloads using ROCm and/or CUDA.
GPU software development using HIP, CUDA, or OpenCL.
Experience developing GPU kernels with HIP, CUDA, or Triton.
Experience in HPC environments.
Effective communication in English.

Responsibilities

Optimize ML models through profiling and bottleneck analysis for GPUs.
Analyze models and optimize compute and memory for training and inference across hardware.
Perform optimization at kernel, framework, and hardware levels.
Work with deep learning and transformer architectures and physics-based simulations.
Profile workloads on hardware, identify bottlenecks, and improve efficiency and scalability.