Senior Data Scientist: Custom Embeddings and Hybrid Search
1 week ago
We embody the spirit of Warren Buffett, a visionary investor who has inspired us to replicate his approach using cutting-edge AI technology. Our team is passionate about breaking down complex tasks into individual components and leveraging sophisticated language models to analyze and understand businesses.
At Fintool, we deconstructed the key tasks that Buffett performs to analyze a company's potential, including reading SEC filings, understanding earnings, and evaluating management decisions. We then built an AI financial analyst to handle these tasks with precision and scale.
Our Philosophy- Small, in-person teams are more effective than large, well-funded companies.
- We avoid meetings and focus on releasing code early, often, and listening to customer feedback.
- We believe high-performing teams do their best work, build long-term relationships, and have the most fun in person.
- We aim to clone and improve the best practices, standing on the shoulders of giants.
- We prioritize speed and iterate quickly, releasing updates for customers as soon as possible.
- We strive to mirror Warren Buffett's virtues, upholding integrity, valuing honesty, and practicing frugality.
Your task will be to develop a high-quality, low-latency retrieval-augmented generation (RAG) system on top of millions of complex documents. You will lead the implementation of custom embeddings, rankers, and hybrid search.
Requirements- Expertise in Python, Machine Learning (embeddings, ranking, recommendations), LLM, and familiarity with Spark, Databricks, and Typescript.
- Minimum 3 years of experience in a related field.
- A competitive salary of $250,000 per year, plus benefits and perks.
- The opportunity to work on a challenging project with a talented team.
- The chance to make a meaningful impact on the development of our AI financial analyst.
Location: San Francisco (no remote work)
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