Quantitative Research Intern (NLP)

AI in this role

hugging-facepytorch

nlp

ROLE/RESPONSIBILTIES:

We are seeking a quant research intern to join an NLP quant team within Point72. We believe the significant advances in NLP methods show promise for finance. We develop and launch end-to-end signals, from data processing to performance testing.

The ideal candidate will have strong machine learning, data science and software engineering skills, some experience with modern NLP, and a curiosity about finance and trading.

Responsibilities may include:

Using NLP to construct features from varied datasets
Formulating research hypotheses to derive alpha
Building and testing the performance of trading signals based on NLP and financial features
Launching identified signals into production

REQUIREMENTS:

Bachelor’s, Master’s or PhD candidate in computer science or other quantitative discipline
Proficient in Python and general software engineering principles (github, testing, dev workflow)
Strong understanding of machine learning and statistics
Prior research experience preferred
Experience with deep learning, specifically NLP (PyTorch, HuggingFace, LLMs, etc.) preferred
Data science stack familiarity preferred
An interest in financial markets
Great attitude
A collaborative mindset
Commitment to the highest ethical standards

ABOUT POINT72:

Point72 is a leading global alternative investment firm led by Steven A. Cohen. Building on more than 30 years of investing experience, Point72 seeks to deliver superior returns for its investors through fundamental and systematic investing strategies across asset classes and geographies. We aim to attract and retain the industry’s brightest talent by cultivating an investor-led culture and committing to our people’s long-term growth.

How we rate this

Quantitative Research Intern (NLP) at Point72 rates 89 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

NLPHugging FacePyTorch

Questions you could be asked

What NLP problem have you worked on, and how did you measure whether it actually worked?
Walk me through how you've used Hugging Face in your day-to-day work.
What are the limits of PyTorch that you've run into, and how did you work around them?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: NLP, Hugging Face, and PyTorch. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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