AI role · Open offer

NLP Engineer — language understanding & LLM product

OpenAI

Full-time
Senior
Applied NLP
Hybrid
New York, NY, United States · Hybrid
New York, United States
USD 170k–235k + equity

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

4 skills2 tools

At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: New York, NY, United States · Hybrid
Compensation: USD 170k–235k + equity
Experience level: Senior
Team: Applied NLP
Benefits & perks

About this role

What the team is looking for and how this position fits.

OpenAI is hiring a Senior NLP Engineer — language understanding & LLM product on the Applied NLP team (Hybrid · New York, United States). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

OpenAI builds frontier language models and product surfaces used worldwide.

NLP Engineer (hybrid NYC): language understanding, LLM product features, and evaluation for US-facing experiences.

Missions

Ship NLP/LLM features with offline and online quality metrics.
Partner with product on safety, latency, and multilingual coverage.

Stack & environment

Python, PyTorch, NLP eval harnesses, Docker.

What we look for

Production NLP or LLM product experience; US work authorization.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
NLP

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.
JupyterTool
Regular use of Jupyter in the LLM & generative AI stack—configuration, debugging, and team conventions.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: NLP or LLM product work with offline metrics and shipped UX.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, NLP, Machine Learning, and Docker.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · New York, United States)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.

Apply

Candidate accounts only: add your headline and bio on , and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

NLP or LLM product work with offline metrics and shipped UX.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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