Associate, Machine Learning Engineer

Description
We 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