Role Overview:

We are looking for ML Engineers to join our team. In this role, you will build end-to-end ML systems, including data pipelines (with an emphasis on synthetic data generation), training, debugging, and performance analysis. You will also fine-tune large language models (LLMs), apply meta-learning techniques, and improve existing Nace.AI models by leveraging the latest research developments.

Key Responsibilities:

• Design, build, and maintain end-to-end ML systems, including synthetic data pipelines, model training, debugging, and performance optimization.
• 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.

Qualifications:

• Hands-on experience training and fine-tuning large language models (LLMs) and vision-language models (VLMs), including practical work with pre-training, instruction tuning, and alignment techniques (RLHF/DPO/PPO).
• Demonstrated experience with the full LLM/VLM training pipeline: data curation and preprocessing, distributed training across multi-GPU/TPU clusters, hyperparameter optimization, and model evaluation.
• Proficient in Python with a strong track record of building substantial projects.
• Solid foundation in computer science fundamentals (data structures, algorithms, design patterns).
• BS degree in CS or related technical field.
• Solid Experience with ML frameworks and libraries (PyTorch, TensorFlow).
• Self-starter comfortable working in a fast-paced, dynamic environment.

Preferred Qualifications:

• MS/PhD in CS or related technical field.
• Familiarity with data processing stacks such as Spark and Airflow.
• Experience with multi-node GPU training.
• Contributor to open-source ML projects.