Machine Learning Engineer (llms)

Who we are: Factored was conceived in Palo Alto, California by Andrew Ng and a team of highly experienced AI researchers, educators, and engineers to help address the significant shortage of qualified AI & Machine-Learning engineers globally. ​We know that exceptional technical aptitude, intelligence, communication skills, and passion are equally distributed worldwide, and we are very committed to testing, vetting, and nurturing the most talented engineers for our program and on behalf of our clients.

We are seeking a highly skilled Machine Learning Engineer with specialized experience in Language Models (LLMs) to join our team. As an MLE with expertise in LLMs, you will be responsible for developing, implementing, and optimizing cutting-edge natural language processing (NLP) solutions to tackle complex problems in various domains. #LI-Remote

What you will be doing:

• Design, implement, and train LLMs to address specific NLP tasks such as text generation, sentiment analysis, named entity recognition, text summarization, question answering, and more.
• Train LLMs on large-scale datasets using state-of-the-art techniques and frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.
• Conduct research to enhance existing LLMs or develop novel models tailored to specific applications.
• Analyze model performance, identify areas for improvement, and iterate on the development process to achieve desired outcomes.
• Collaborate with software engineers and developers to deploy LLM-based solutions into production environments.

What you must bring:

• 5+ years of experience in machine learning and deep learning, with a focus on NLP.
• In-depth understanding of LLMs and their architectures, including hands-on experience with transformer-based models (e.g., BERT, GPT).
• Strong knowledge and practical experience in training Deep Neural Networks.
• Strong proficiency in programming languages such as Python, along with experience using libraries and frameworks like TensorFlow, PyTorch, scikit-learn, and NLTK.
• 2+ years fo experience with Convolutional Neural Networks (CNNs) or Vision Transformers for image processing.
• Solid understanding of machine learning principles and techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning.
• Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
• Familiarity with Ray Serve and openness to adopt other distributed serving technologies (Kubeflow, BentoML, Triton, etc).
• Understanding of distributed systems and their application in machine learning.
• Knowledge and practical experience in implementing CI/CD pipelines for machine learning projects.
• Strong communication skills in English.