Machine Learning Engineer

• Guarantee the stability of inference and training processes across various hardware and software configurations, including:

• CPU Hardware:

• Popular consumer and server-grade processors from major vendors (Intel, AMD, Apple)
• GPU/AI Accelerators:

• Leading solutions from NVIDIA (CUDA, Triton Inference Server), AMD (ROCm), Intel (Arc, NPU), and Apple (Metal/CoreML)
• Model Formats:

• Industry-standard formats like GGUF and PyTorch
• Operating Systems:

• Windows, macOS, and Linux
• Inference Engines:

• Widely used engines like llama.cpp, vLLM, and TensorRT-LLM
• Training Frameworks:

• Popular frameworks like PyTorch and MLX
• (Bonus) Expertise in edge training/fine-tuning, micro training, and federated training across diverse hardware and software AI stacks.

• Solid software engineering skills with proven experience in Python and C++
• Effective communication skills in English
• Familiarity with essential development tools and workflows like Git, CI/CD, and Agile methodologies
• Experience in hardware optimization for CPUs, GPUs, and NPUs
• Exposure to large language models (LLMs), including popular foundation models like Llama2, Mistral, and GPT-2
• (Bonus) Experience deploying LLMs/AI models in production environments (server/edge)

• Competitive all-inclusive compensation package
• Generous leave policy (14 days + unlimited sick leave)
• Annual equipment budget available after probation period

NodeFlair