ML Engineer: LLMs & Quantum AI, Hybrid Role

RemoteStar; España; Hybrid; EUR 60,000 - 90,000; Full time

Benefits offered by this job

Signing bonus

Relocation package

Private health insurance

Educational budget

Language classes and discounted lunch

High paced environment

Job summary

RemoteStar is seeking an experienced Machine Learning Engineer with a strong background in Large Language Models to lead design, implementation, and improvement of language models. You will work across cross-functional teams to integrate models into products, exploring quantum-inspired approaches and cutting-edge AI research.

The role emphasizes building LLM-based applications, benchmarking performance, and collaborating in a fast-paced environment while contributing to the advancement of NLP

Qualifications

• Bachelor's/Master's/PhD in AI/CS/DS or related field.
• 5+ years designing or fine-tuning DL/transformer models.
• Experience with transformer libraries (HuggingFace, etc.).
• Strong math and theory in DL and neural networks.

Responsibilities

• Design and implement methods to optimize and compress LLMs.
• Benchmarks and evaluations to improve accuracy and efficiency.
• Build LLM-based apps such as RAG and AI agents.
• Collaborate with cross-functional teams and document experiments.
• Guide junior members and participate in code reviews.

Skills

Deep learning

Transformer models

Python programming

GPU architectures

Experimentation & debugging

Communication skills

Education

Bachelor/Master/PhD in AI/CS/DS

Tools

PyTorch

HuggingFace

Accelerate

Datasets

Docker

AWS

Job description

RemoteStar is seeking an experienced Machine Learning Engineer with a strong background in Large Language Models to lead design, implementation, and improvement of language models. You will work across cross-functional teams to integrate models into products, exploring quantum-inspired approaches and cutting-edge AI research.

The role emphasizes building LLM-based applications, benchmarking performance, and collaborating in a fast-paced environment while contributing to the advancement of NLP