Job DescriptionWe are seeking an early-career engineer to join our team and play a vital role in developing and enhancing AI-powered applications for our financial services business. The ideal candidate will have a solid foundation in software development, hands-on experience with modern AI tools, and a keen interest in understanding the behavior of language models in real-world applications. As an Associate, you will have the opportunity to work closely with our experienced engineers and contribute to the growth and success of our innovative AI initiatives.
Responsibilities- Collaborate with a cross-functional team to build, evaluate, and improve AI-powered financial services applications.
- Design and implement machine learning models and algorithms to solve complex business problems.
- Work with large language models (LLMs) and understand their behavior and potential failure modes.
- Conduct testing and evaluation of LLM-powered applications, analyzing failures and defining success metrics.
- Apply machine learning, statistics, and experimental design principles to reason about model behavior.
- Communicate effectively with product, engineering, and business partners to align on project goals.
- Ensure responsible AI practices are followed, considering privacy, security, and appropriate automation.
- Stay updated with the latest advancements in AI and machine learning technologies.
- Document and present project progress and findings to stakeholders.
- Provide support and mentorship to junior team members as needed.
Qualifications- Bachelor's degree in a technical field (computer science, machine learning, mathematics, etc.) or equivalent practical experience.
- Experience contributing to production-level software development, internships, research, or substantial personal projects.
- Strong programming skills in Python, with a focus on writing clear, tested, and maintainable code.
- Hands-on experience with web services, data integration, testing, logging, and monitoring.
- Practical knowledge of building with LLMs and understanding common failure modes.
- Ability to test, evaluate, and improve LLM-powered applications.
- Grounding in machine learning, statistics, and experimental design, with a knack for technical documentation.
- Excellent communication skills and a collaborative mindset.
- Interest in applying AI responsibly in financial services.
- Familiarity with agentic workflows, evaluation tools, and cloud deployment is a plus.
Compensation
