NLP Research Engineer

Nym

. Research complex problems related to state-of-the-art NLP, GNNs, and Tabular data .

Posted 10/22/2026full-timeTel Aviv-Yafo • IsraelMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in Deep Learning algorithms and frameworks, with a focus on fine-tuning and evaluating large language models. Proficient in Python and LLM frameworks, capable of integrating research into production-quality systems while conducting thorough error analysis.

Highest-signal resume keywords

Deep Learning AlgorithmsLarge Language Models (LLMs)Python ProgrammingError Analysis SkillsHugging Face Transformers

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Deep LearningNLP AlgorithmsModel EvaluationPrompt EngineeringInstruction TuningParameter-Efficient MethodsAgentic Systems DesignMulti-Step PlanningError DiagnosisProduction-Quality Code

Tools & Technologies

Hugging Face TransformersLangChain

Certifications & Qualifications

MSc in Computer ScienceMSc in Statistics

Industry Keywords

NLPGNNsTabular DataResearch EngineeringData Science

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

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 research work into Nym’s existing product while demonstrating its value

Requirements

What you’ll need

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