NLP Engineer

AI in this role

ragfine-tuningnlp

ROLE/RESPONSIBILTIES:

We are seeking a NLP engineer for a NLP research team within Point72. The ideal candidate has a track record of developing modern NLP solutions, including the use of LLMs, agents, RAG, open-source models fine-tuning and deployment, etc.

Responsibilities include:

Exploring and combining rich internal and external textual datasets
Building GenAI solutions, leveraging both internal models and external APIs
Formulating research hypothesis to derive alpha
Post-training models for specific tasks
Benchmarking LLM approaches internal tasks

We believe the investment world offers unique opportunities to use modern NLP methods. We are a team ideally situated for that, having access to both internal and commercial datasets, with an end-to-end research philosophy and direct portfolio impact.

REQUIREMENTS:

Bachelor, Master’s or PhD graduate or candidate in computer science or another quantitative discipline
Experience in NLP, particularly LLMs.
Proficient in Python, SQL and general software engineering principles (github, testing, etc.)
Data science stack familiarity
Interest in financial markets
Collaborative mindset

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

NLP Engineer at Point72 rates 90 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

RAGFine TuningNLP

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
What NLP problem have you worked on, and how did you measure whether it actually worked?
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: RAG, Fine Tuning, and NLP. 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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