Artificial Intelligence Engineer

AI Performance Engineer

We are looking for a AI Performance Engineer in Latin America to work on latest large AI model knowledge, deep learning performance optimization and benchmarking on modern GPU-based systems, with a strong focus on MLPerf Training and Inference workloads.

The primary models we work on include Llama 2, Llama 3, DeepSeek, and open-source GPT-style models (GPT-OSS).

This is a hands-on engineering role involving performance profiling, PyTorch optimization, large-scale distributed training, and building reproducible benchmarking environments, in close collaboration with other performance- and systems-focused engineers.

What You Will Do

• Optimize training and inference pipelines for large language models such as Llama 2, Llama 3, DeepSeek, and GPT-OSS
• Work on MLPerf Training and/or Inference benchmarks for LLM workloads
• Profile GPU workloads to identify compute, memory, and communication bottlenecks
• Improve scaling efficiency across multi-GPU and multi-node setups
• Tune distributed training strategies (DDP, FSDP, ZeRO, tensor/pipeline parallelism)
• Build and maintain reproducible benchmark environments (Docker / Singularity)
• Collaborate with engineers on performance, stability, and scalability improvements
• Document findings and contribute to benchmark submissions and internal reports

What We Expect (Required)

• 1-2 year of AI engineering knowledge / Deep Learning, GPU, or HPC-related roles
• Strong Python skills and solid experience with PyTorch
• Hands-on experience with LLM training or inference (Llama, GPT-style models, or similar)
• Experience with distributed training (DDP, FSDP, ZeRO, DeepSpeed, or equivalent)
• Good understanding of GPU performance fundamentals (compute vs memory, profiling, optimization)
• Experience working in Linux-based environments
• Familiarity with container technologies (Docker or similar)
• Good level of spoken and written English

Nice to Have (Strong Plus)

• Experience working with MLPerf or other standardized benchmarking frameworks
• Exposure to LLM optimization techniques (activation checkpointing, KV-cache optimization, sequence parallelism)
• Experience with GPU profiling tools (torch.profiler, Nsight, or equivalent)
• Knowledge of GPU kernel optimization (CUDA, HIP, Triton, or similar)
• Experience working with job schedulers (Slurm or equivalent)
• Familiarity with quantization or mixed precision (FP16, BF16, FP8)