Job Description
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Company Overview
A leading financial technology company is building advanced financial products and intelligent systems designed to make investing and financial services more accessible. The organization operates in a highly data-rich and regulated industry, using modern technology and artificial intelligence to develop products that serve millions of customers.
The AI R&D team focuses on developing and scaling high-impact machine learning models that power personalization, search, social feeds, fraud detection, and risk management. The team works closely with product, growth, data engineering, and platform teams to transform complex financial data into intelligent systems that improve customer experiences.
As a Staff Machine Learning Engineer, AI R&D, the successful candidate will serve as a technical leader responsible for designing and delivering sophisticated personalization and recommendation systems that influence what millions of users see and experience on the platform.
The role will involve providing technical direction, mentoring engineers, collaborating with cross-functional teams, and advancing the use of modern machine learning technologies, including agentic workflows and large language model fine-tuning. The position offers an opportunity to work on technically complex systems with significant product impact in a highly regulated financial environment.
Key Responsibilities
- Design, build, and deploy end-to-end personalization, ranking, and recommendation systems supporting core financial technology products, including growth, social feeds, and search.
- Own the complete machine learning lifecycle, from feature engineering and model development through deployment, monitoring, and optimization.
- Partner closely with product, data engineering, and platform teams to define technical strategies and scope complex machine learning initiatives.
- Lead the execution of multiple technical workstreams while maintaining high standards for quality, scalability, and reliability.
- Drive zero-to-one development of new machine learning capabilities by prototyping, iterating, and scaling production-grade models.
- Develop solutions for complex machine learning problems within a high-stakes financial environment where data quality and regulatory requirements are critical.
- Evaluate and integrate modern artificial intelligence approaches, including agentic workflows and LLM fine-tuning, into existing machine learning systems.
- Establish strong engineering standards through architecture reviews, code reviews, technical guidance, and mentorship.
- Help advance the technical capabilities, efficiency, and velocity of the broader AI R&D organization.
- Collaborate with cross-functional stakeholders to translate ambitious ideas into scalable production systems.
Required Qualifications
- 10+ years of experience as a Machine Learning Engineer or in a closely related field.
- Strong foundation in machine learning fundamentals, including ranking, recommendation systems, deep learning, and optimization.
- Proven track record of deploying and operating machine learning models in production at scale.
- Demonstrated expertise in personalization and recommendation systems.
- Experience owning personalization or recommendation systems end-to-end within a high-traffic, data-rich environment such as fintech, e-commerce, social platforms, or a comparable industry.
- Proven ability to take ambiguous and high-impact problems from concept through production implementation.
- Experience delivering production-grade machine learning solutions with measurable business or product results.
- Exposure to or hands-on experience with agentic systems, LLM fine-tuning, or other modern AI paradigms.
- Strong technical judgment regarding when modern AI approaches should and should not be applied.
- Master’s degree in Computer Science, Statistics, or a related technical field, or equivalent professional experience.
- Strong programming skills in Python.
- Familiarity with machine learning infrastructure and related engineering tools.
- Strong communication, collaboration, technical leadership, and mentoring capabilities.
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