Overview
Mozilla.ai is at the forefront of the AI revolution, advocating for a decentralized and open-source approach. Our ambition is to empower developers to craft AI solutions that are both scalable and trustworthy. Through innovation, collaboration, and responsible AI practices, we’re shaping an AI future anchored in user agency, privacy, and transparency.
Position: Machine Learning Engineer
Location: Remote (EMEA, Canada, USA)
Type: Full-Time
Position Overview:
We are seeking a skilled Machine Learning Engineer with a strong background in Natural Language Processing (NLP) and Large Language Models (LLMs) to join our team. The ideal candidate will work with foundational OSS language models and specialize them. The candidate will prepare datasets for fine-tuning, evaluate results, and serve the models for production. The main responsibilities of this position are:
Develop and implement use cases involving language model fine-tuning and validation.
Prepare and preprocess datasets for model fine-tuning and training.
Fine-tune OSS foundational models for domain specific tasks.
Evaluate model performance using standard metrics and techniques.
Design and deploy scalable and efficient NLP pipelines on our cloud infrastructure.
Develop APIs for dataset management and model serving.
Collaborate with cross-functional teams to integrate NLP functionalities into the main product.
Engage with internal and external stakeholders, translating complex technical details into clear insights.
Contribute to the product engineering lifecycle, from ideation to deployment and maintenance of new features.
Comfortable working on tooling to enable any of the above
Company:
Mozilla.AI
Qualifications:
Strong background in machine learning, NLP, and deep learning.
Proven experience developing and fine-tuning language models, particularly using PyTorch.
Experience with large-scale dataset processing and data augmentation techniques.
Experience with platforms like Weight & Biases for experiment tracking and VectorDB for embedding storage is plus.
Proficiency in Python and familiarity with relevant libraries and tools.
Knowledge of APIs and cloud computing platforms (e.g., AWS, GCP), with containerization technologies (e.g., Docker) being a plus.
Excellent problem-solving skills and the ability to work both independently and as part of a team.
Effective communication skills, including the ability to translate technical concepts to non-technical stakeholders.
A demonstrated track record of delivering high-quality, scalable solutions in a fast-paced environment.