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Lead Machine Learning Research Engineer, Generative Ai
Company | Scale AI |
Address | San Francisco, CA, United States |
Employment type | FULL_TIME |
Salary | |
Category | Software Development |
Expires | 2023-07-12 |
Posted at | 11 months ago |
Scale's Generative AI Data Engine powers the most advanced LLMs and generative models in the world through world-class RLHF/RLAIF, data generation, model evaluation, safety, and alignment.
- Led a team of highly effective researchers and engineers. Provide guidance, mentorship, and technical leadership to a team of researchers and engineers working on Generative AI projects. Develop and evaluate methods for integrating machine learning into human-in-the-loop labeling systems to ensure high-quality and throughput labels for our customers.
- Be able and willing to multi-task and learn new technologies quickly.
- Work with massive datasets to develop both generic models as well as fine-tune models for specific products.
- Work with customers and 3rd party research groups to understand their goals and define how we can enable them.
- Build a scalable ML platform to automate our ML services, including automated model retraining and evaluation.
- Work with product and research teams to identify opportunities for improvement in our current product line and for enabling upcoming product lines.
- Implement and improve on state-of-the-art models developed internally and from the community and put them into production to solve problems for our customers and taskers.
- 5+ years of experience using LLM, deep learning, deep reinforcement learning, or natural language processing in a production environment. Especially training foundational AI models through pre-training, fine-tuning, and RLHF.
- Solid background in algorithms, data structures, and object-oriented programming
- Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment
- Degree in computer science or related field
- Experience with MLOps and the automation of model training & evaluation
- Deep appreciation for building high-quality, robust, reusable machine-learning software
- Strong programming skills in Python, experience in PyTorch or Tensorflow
- A vision for where the field should go and what Scale should do to enable it.
- Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization
- Publication experience in the field or related topics.
- Experience with model optimization techniques for both training and inference
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