Machine Learning Engineer

Responsibilities

• Design, build, and maintain end-to-end ML systems, including synthetic data pipelines, model training, debugging, and performance evaluation
• Fine-tune large language models (LLMs) and implement meta-learning methods to enhance model generalization and efficiency
• Improve existing Nace.AI models by incorporating advancements from recent ML research

Requirements

• Hands-on experience training and fine-tuning LLMs and VLMs (pre-training, instruction tuning, alignment techniques like GRPO, RLHF, DPO, or PPO)
• Experience with Deep Learning Models, especially Transformers
• Ability to translate research papers into production-ready code
• Proven experience scaling inference infrastructure for LLMs/VLMs using frameworks like vLLM or TGI
• Proficiency in Python and strong computer science fundamentals
• BS degree in CS or a related technical field
• Experience with ML frameworks such as PyTorch or TensorFlow

Preferred Qualifications

• MS or PhD in CS or a related technical field
• Familiarity with Spark and Airflow
• Experience with multi-node GPU training
• Contributor to open-source ML projects
• Deep knowledge in Linear Programming
• Experience with advanced NLP and Multimodal post-training (distillation, quantization, deployment optimization)
• Expertise in inference time optimization and quantization techniques (AWQ, GPTQ, FP8/GGUF)