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Company | Fusemachines |
Address | Greater Toronto Area, Canada |
Employment type | FULL_TIME |
Salary | |
Category | Software Development |
Expires | 2023-07-12 |
Posted at | 11 months ago |
About Fusemachines
- Train and fine-tune LLMs to achieve optimal performance in various NLP tasks, such as text generation, sentiment analysis, and named entity recognition.
- Conduct research and stay updated with the latest advancements in the field of NLP and LLMs, and apply relevant techniques to enhance our existing models.
- Design and develop state-of-the-art NLP models and systems utilizing LLM techniques.
- Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to understand business requirements and develop NLP solutions.
- Optimize NLP models for performance, scalability, and efficiency to handle large-scale datasets. Implement and maintain deployment pipelines for NLP models in a cloud environment, particularly AWS.
- Mentor and provide guidance to junior NLP engineers, assisting them in their professional growth.
- Ensure the security and privacy of data throughout the development and deployment processes.
- 5+ years of industry experience
- Solid understanding of NLP concepts, techniques, and algorithms, such as word embeddings, attention mechanisms, and sequence labeling.
- A Ph.D. is a plus. years of industry experience in NLP engineering, with a focus on LLMs.
- Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.
- Proficiency in programming languages such as Python, with experience in NLP libraries/frameworks like TensorFlow, PyTorch, or Hugging Face. Experience with cloud platforms, specifically AWS, and deploying NLP models in a cloud environment.
- Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
- Experience in leading and mentoring a team of NLP engineers is a plus.
- Strong communication skills and the ability to effectively convey complex technical concepts to both technical and non-technical stakeholders.
- Strong knowledge and hands-on experience in training and fine-tuning LLMs, particularly models like GPT-3, Bloom, and BERT.
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