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Open nowFirst seen 3 days ago

NLP Research Engineer

Nym Health4 open roles

Where
TLV
Work mode
Hybrid
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Your applicationOpen nowNLP Research EngineerNym Health · TLV

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: first seen 3 days ago

The posting

At Nym, we’re simplifying healthcare by automating the accurate interpretation of clinical language.

Our interdisciplinary team has developed technology that can understand clinical language and provides an entirely new way of analyzing and decoding medical records into clear, concise, and actionable information. This ultimately ensures clinical and administrative teams can spend more time focused on patient care.

Our engine currently processes over six million charts annually in more than 300 emergency department and urgent care settings across the US. We are backed by top-tier VCs and growing rapidly, with offices in NYC and Tel Aviv.

Roles and Responsibilities

We are looking for a NLP Research Engineer to join our Research team. In this role, you will work on Nym’s core business challenges, tackling tough and diverse algorithmic problems using the latest NLP research.

As the NLP Research Engineer, you will:

  • Research complex problems related to state-of-the-art NLP, GNNs, and Tabular data.
  • Implement algorithms and train models to solve problems at the core of Nym’s product.
  • Integrate your work into Nym’s existing product while demonstrating its value.

Requirements

  • 3+ years of experience as a researcher/research engineer/data scientist.
  • 2+ years of experience with Deep Learning algorithms and frameworks.
  • Hands-on experience fine-tuning and evaluating large language models (LLMs), including prompt engineering, instruction tuning, and parameter-efficient methods such as LoRA.
  • Experience designing and building LLM-based agentic systems, including tool use, multi-step planning, and agent evaluation.
  • Strong error analysis skills, with the ability to diagnose model failures, identify patterns in errors, and translate findings into targeted improvements.
  • Strong software engineering skills in Python and LLM frameworks such as Hugging Face Transformers and LangChain, with a track record of writing clean, scalable, production-quality code for large-scale experiments.
  • MSc in Computer Science / Statistics is an advantage.
  • Basic knowledge of state-of-the-art NLP algorithms is an advantage.
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