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Engineering new possibilities with platforms, data, and generative AI
Generative AI Scientist
Location
United States
Posted
166 days ago
Salary
0
Seniority
Senior
Job Description
Generative AI Scientist
Egen
• Join our growing team focused on building Generative AI applications for document summarization, classification, and question and answer using unstructured and structured data. • Develop applications on Google Cloud. • Work with customers to understand their requirements. • Suggest new ideas and features to the team. • Improve the product and implement new functionalities with a passion for quality. • Implement best practices and state-of-the-art techniques for AI and LLMs. • Work on research, experimentation, and implementation of novel methods.
Job Requirements
- Required hands-on experience designing and implementing applications using Large Language Models (LLM).
- Python programming.
- Google Gemini, OpenAI GPT, LLaMA, or similar models.
- LangChain, LlamaIndex, or similar frameworks.
- Advanced prompt engineering.
- Retrieval Augmented Generation (RAG).
- Vector databases (GCP Vector Search, ChromaDB, Pinecone, pgvector, or similar).
- The ideal candidate will also have experience in one or more of the following:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
- LLM model fine-tuning.
- Embedding model fine tuning.
- Shell scripting.
- Google Cloud Vertex AI (AutoML, AI APIs, etc.).
- Classic Machine Learning (ML frameworks, neural net models development, training, tuning, serving).
- MLOps (ideally on GCP).
- Data Engineering (including SQL).
Benefits
- Comprehensive Health Insurance
- Paid Leave (Vacation/PTO)
- Paid Holidays
- Sick Leave
- Parental Leave
- Bereavement Leave
- 401 (k) Employer Match
- Employee Referral Bonuses
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• Drive Applied Research: Lead the design, training, and evaluation of large language models to solve healthcare-specific challenges, advancing the state of the art in clinical Natural Language Understanding. • Leverage Human-in-the-Loop Feedback: Work closely with cross-functional teams to integrate Human-in-the-Loop data, using it to guide model improvements and explore new methods for optimizing performance. • Collaborate Across Teams: Partner with healthcare experts and other stakeholders to integrate qualitative insights, ensuring models align with real-world needs and deliver meaningful results. • Stay on the Cutting Edge: Regularly evaluate advancements in ML to determine their relevance to our work, maintaining AKASA’s leading edge in responsible, high-impact healthcare AI. • Contribute to Broader Impact: Publish and share research findings in the broader AI community, helping to advance healthcare applications of AI through peer-reviewed publications.
