Our mission at Gretel.ai is to build an open ecosystem around safe, anonymized data. To do this, we believe that technical breakthroughs are needed in generative models, differential privacy, and federated learning through a generalized approach that will work with any kind of data- be it text, structured data, audio, or video.
As an Applied Scientist, you will be working on ways to make privacy engineering automated to people everywhere. You'll be building cutting edge technology to unlock real-life use cases for synthetic data that our customers are working on today-- including models that can better detect heart disease across genders and ethnicities, financial models that can better respond to unseen data and market changes, and safe datasets that enable medical researchers to share data on rare diseases such as Lymphoma without compromising patient identity.
We expect you to
- Have a track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects.
- Be involved in end-to-end development, exploring new applications and techniques within language modeling, synthetic data generation, and privacy enhancing technologies.
- Conduct research to improve the performance of large ML and AI models using a diverse collection of datasets and community feedback.
- M.S. or PhD in Computer Science, related technical field or equivalent practical experience.
- Strong communication skills - you speak, and write, your mind well. We’re a distributed team so we’re extra mindful about communication.
- At least 5 years of professional experience.
Nice to have
- Past experience in creating high-performance implementations of deep learning algorithms
- Deep experience with ML frameworks such as TensorFlow, HuggingFace, PyTorch, OpenAI
- Deep experience with ML algorithms such as Transformers, LSTM, RNN, language models, NLP
- Experience working remotely in a distributed company