Senior Machine Learning Engineer

Grid Dynamics; Murcia; Presencial; EUR 70.000 - 110.000; Jornada completa

Descripción de la vacante

Grid Dynamics is seeking a Senior Machine Learning Engineer - LLM Systems & Evaluation to lead ML projects, design evaluation methods, and build datasets and benchmarks.

You will evaluate LLM-based systems, including RAG apps, agents, and safety-focused components, and collaborate with product and research teams to translate goals into measurable ML objectives.

Formación

• 5+ years of ML engineering experience.
• Strong Python and ML framework skills.
• Experience with LLMs, RAG, agents, and AI safety concepts.

Responsabilidades

• Own ML projects from problem definition through implementation
• Design and implement evaluation methodologies for AI and ML systems
• Create datasets, benchmarks, and metrics to measure model and product performance
• Evaluate and improve LLM-based systems, including RAG applications, agents, safety systems, and end-to-end AI products
• Analyze model behavior, identify failure modes, and recommend practical improvements
• Build and maintain ML pipelines, tooling, and evaluation infrastructure
• Collaborate with product, engineering, and research teams to translate business goals into measurable ML objectives
• Prototype and iterate rapidly to solve business and product challenges
• Communicate findings, trade-offs, and recommendations to both technical and non-technical stakeholders

Conocimientos

5+ years ML

Python

ML fundamentals

Model evaluation

Communication

Ambiguity handling

LLM safety concepts

Herramientas

PyTorch

TensorFlow

JAX

Descripción del empleo

Descripción del trabajo

We are looking for a talented Senior Machine Learning Engineer - LLM Systems & Evaluation.

This is an opportunity to work on next-generation AI systems, including large language models, retrieval-augmented generation, agents, and AI safety-focused evaluation.

Essential functions:

• Own machine learning projects from problem definition through implementation
• Design and implement evaluation methodologies for AI and machine learning systems
• Create datasets, benchmarks, and metrics to measure model and product performance
• Evaluate and improve LLM-based systems, including RAG applications, agents, safety systems, and end-to-end AI products
• Analyse model behaviour, identify failure modes, and recommend practical improvements
• Build and maintain ML pipelines, tooling, and evaluation infrastructure
• Collaborate with product, engineering, and research teams to translate business goals into measurable ML objectives
• Prototype and iterate rapidly to solve business and product challenges
• Communicate findings, trade-offs, and recommendations to both technical and non-technical stakeholders

Qualifications:

• 5+ years of experience in Machine Learning Engineering or a related field.
• Strong understanding of machine learning fundamentals and model evaluation
• Strong Python programming skills and experience with modern ML frameworks such as PyTorch, TensorFlow, or JAX
• Understanding of retrieval-augmented generation (RAG), agentic systems, and LLM safety concepts
• Experience training, fine-tuning, or adapting machine learning models
• Experience working with Large Language Models beyond simple API integration
• Experience evaluating AI systems and translating results into actionable recommendations
• Experience building and maintaining machine learning systems and pipelines
• Ability to work effectively in ambiguous problem spaces with incomplete requirements and limited data
• Strong written and verbal communication skills

Would be a plus:

• Experience designing benchmarks, evaluation frameworks, or automated evaluation systems
• Experience with distributed training or large-scale model inference
• Experience building reusable ML tooling and internal platforms
• Experience with cloud platforms and modern MLOps practices
• Experience working on user-facing AI products at scale
• Research experience or publications in machine learning or AI-related fields