- Страна
- США
- Зарплата
- 30 $ – 35 $
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на вакансии с ИИ

2026 AI Initiatives PhD Internship
Отличная возможность для PhD-студентов поработать над передовыми ИИ-решениями в финансовом секторе. Высокая почасовая оплата для интернатуры и четкие перспективы карьерного роста в престижной компании.
Сложность вакансии
Высокая сложность обусловлена требованием степени PhD в количественных науках и необходимостью глубоких знаний в области LLM и NLP. Кандидат должен сочетать в себе навыки исследователя и инженера, способного работать с реальными финансовыми данными.
Анализ зарплаты
Предлагаемая ставка $30-$35 в час является конкурентоспособной для PhD-интернов в США, особенно в сфере финансовых технологий. Это соответствует средним рыночным ожиданиям для исследовательских стажировок в крупных инвестиционных компаниях.
Сопроводительное письмо
I am writing to express my strong interest in the 2026 AI Initiatives PhD Internship at StepStone. As a PhD candidate in a quantitative field with a deep focus on machine learning and financial modeling, I am particularly drawn to StepStone’s mission of leveraging LLMs and advanced NLP to drive strategic outcomes in private markets. My background in developing complex data extraction pipelines and optimizing retrieval systems aligns perfectly with the technical challenges your team is tackling.
During my doctoral research, I have honed my skills in Python and statistical modeling, working extensively with large-scale datasets to uncover actionable insights. I am impressed by StepStone's collaborative, 'low-ego' culture and am eager to contribute to the AI project lifecycle, from metadata extraction to portfolio risk assessment. I am excited about the prospect of bringing my technical expertise to the La Jolla office and learning from your world-class team of specialists.
Составьте идеальное письмо к вакансии с ИИ-агентом

Откликнитесь в stepstone уже сейчас
Присоединяйтесь к команде StepStone и примените свои знания в области ИИ для трансформации мировых финансовых рынков!
Описание вакансии
We are global private markets specialists delivering tailored investment solutions, advisory services, and impactful, data driven insights to the world’s investors. Leveraging the power of our platform and our peerless intelligence across sectors, strategies, and geographies, we help identify the advantages and the answers our clients need to succeed.
The team you'll join
StepStone's AI Initiatives team focuses on harnessing AI and machine learning to transform private markets investment research. The team spans the entire AI project lifecycle—from identifying high-impact use cases to deploying production-ready solutions—integrating cutting-edge technologies like LLMs, NLP, and advanced quantitative methods to drive strategic outcomes across portfolio analysis, investment performance, and risk assessment.
About the role
StepStone is seeking highly analytical and technically skilled interns to join the AI Initiative team. This role is best suited for candidates with strong coding expertise, advanced quantitative skills, and an interest in applying AI/LLMs to financial markets. Interns will work on research-driven projects, model development, and large-scale data analysis.
We are looking for team players with a collaborative mindset and a strong work ethic—candidates who take ownership of their work but remain open to feedback, eager to learn from others, and committed to collective success. A low-ego, problem-solving approach is essential for success in this role.
What you'll do
Interns will develop and optimize AI/ML solutions—including LLM applications, data extraction pipelines, and retrieval systems—to enhance investment research and portfolio analytics. You'll work hands-on with large financial datasets, applying quantitative modeling techniques to support investment performance analysis and risk assessment across StepStone's private markets portfolio.
Key responsibilities
- Develop and optimize financial models using Python, R, or VBA.
- Apply AI/ML techniques such as metadata extraction, vector length optimization, and retrieval optimization to enhance investment research.
- Analyze large financial datasets and apply statistical modeling techniques to identify market trends.
- Support quantitative research on investment performance, risk analysis, and portfolio construction.
- Document technical findings and communicate insights to internal teams.
What we're looking for
- Pursuing a Ph.D. in Mathematics, Physics, Econometrics, Statistics, Engineering, or a related quantitative field.
- Strong coding experience (Python, R, VBA), with demonstrated ability to work with large datasets.
- Experience or coursework in AI/ML (LLMs, NLP, data retrieval, vector embeddings) is highly preferred.
- Strong problem-solving skills with a detail-oriented, hands-on approach to work.
- Team-oriented and adaptable—able to balance independent work with collaborative projects.
- Open to feedback and continuous improvement, with a strong intellectual curiosity.
- Knowledge of SQL, Power BI, or cloud computing is a plus.
- Willingness to contribute at all levels—whether tackling complex modeling or assisting with data prep, no task is too small.
- We are open to interns working remotely to begin. Interns will be required to relocate to our La Jolla office starting June 2026.
Please note that Interns will work in-person in our La Jolla office by June 1, 2026.
Why join us?
At StepStone, we foster a supportive and inclusive team culture where collaboration and learning are the core of what we do. Our collegial atmosphere encourages teamwork, mentorship, and professional development. This summer internship is a great opportunity to gain practical experience and develop critical skills that will help you launch a successful career in private markets.
Salary: $30 - $35 per hour
Click here to learn more about the intern experience.
The salary is an estimate of pay for this position. Actual pay may vary depending on job-related factors that can include location, education, skill, and experience. The salary does not include any benefits or other forms of possible compensation that may be available to employees.
#LI-Hybrid
At StepStone, we believe that our people are our most important asset and crucial to our success. We are an Equal Opportunity Employer that strives to create an environment that empowers our employees and allows them to be heard, regardless of title or tenure. Our organizational community features multiple Employment Resource Groups as well as mentorship programs to enhance the employee experience for all.
As an Equal Opportunity Employer, StepStone does not discriminate on the basis of race, creed, color, religion, sex, national origin, citizenship status, age, disability, marital status, sexual orientation, gender identity, gender expression, genetic information or any other characteristic protected by law.
Candidates must be at least 18 years old to apply.
Создайте идеальное резюме с помощью ИИ-агента

Навыки
- Python
- R
- VBA
- LLM
- NLP
- SQL
- Power BI
- Machine Learning
- Statistical Modeling
- Econometrics
- Vector Embeddings
Возможные вопросы на собеседовании
Проверка глубины понимания современных архитектур ИИ, критически важных для данной роли.
Можете ли вы объяснить разницу между различными методами оптимизации поиска (retrieval optimization) в контексте RAG-систем для финансовых документов?
Оценка практического опыта работы с данными, упомянутыми в описании вакансии.
Расскажите о самом сложном наборе данных, с которым вы работали. С какими проблемами очистки или структурирования вы столкнулись?
Проверка способности применять теоретические знания PhD к бизнес-задачам компании.
Как бы вы подошли к задаче автоматического извлечения метаданных из неструктурированных отчетов об инвестициях в частный капитал?
Оценка навыков программирования и владения инструментарием.
Какие библиотеки Python вы предпочитаете использовать для статистического моделирования и почему? Можете ли вы привести пример оптимизации производительности вашего кода?
Проверка соответствия корпоративной культуре 'low-ego' и командной работе.
Опишите ситуацию, когда вы получили критическую обратную связь по вашему исследованию или коду. Как вы на это отреагировали и что изменили?
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- 30 $ – 35 $