About the Job:
The company is on a mission to provide people with enhanced visibility, control, and freedom over their finances. Since its inception in 2015, the company has grown rapidly, now serving over 65 million customers with a range of products that enable better management of money, from spending and saving to investing and exchanging.
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The Data Science team at the company is tasked with solving complex problems and improving customer experiences. As a Deep Learning Engineer, you will work on integrating GenAI and large language models (LLMs) to build transformative products, including user-facing tools and advanced process optimizations.
Key Responsibilities:
- Build AI-driven features from scratch, such as personal assistants, chatbots, and copilots.
- Develop user-focused and backend features using deep learning.
- Deliver impactful, scalable, data-driven AI solutions.
- Collaborate with Product, Engineering, and Data teams to tackle deep learning challenges.
- Integrate cutting-edge AI technologies to foster innovation at the company.
What You'll Need:
- Experience in deep learning within the natural language processing area and LLMs.
- A bachelor's degree in a STEM field (mathematics, computer science, engineering).
- Excellent knowledge of data science tools, particularly Python and SQL.
- A strong understanding of probability and statistics fundamentals.
- Big-picture thinking for diagnosing problems and productionizing research.
- Excellent communication and collaboration skills with Product Owners and business heads.
Nice to Have:
- A master's or PhD in a quantitative discipline.
- Proficiency in additional programming languages like Java, Scala, or C++.
- Experience working at a large tech company with a valuation of over $15 billion.
- Participation in school/university competitions in physics, math, economics, or programming.
The company is committed to creating an inclusive workplace and encourages applications from individuals with diverse backgrounds and experiences.
Language Requirements: Not specified.
