Machine Learning Engineer, Senior

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

Descripción de la vacante

Grid Dynamics Tarragona is seeking a Senior Machine Learning Engineer specializing in LLM systems and evaluation. You will work on next-generation AI systems, including RAG applications and safety-focused evaluation, translating business goals into measurable ML objectives.

You will own projects end-to-end, design evaluation methods, build datasets and pipelines, and collaborate with product, engineering, and research teams.

Formación

• ~5+ years of experience in machine learning engineering or related field.
• ~Strong understanding of ML fundamentals and model evaluation.
• ~Experience with Large Language Models beyond simple API integration.

Responsabilidades

• Own ML projects from problem definition through implementation.
• Design 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.

Conocimientos

Python

ML frameworks

LLM evaluation

RAG systems

Experimentation

Communication skills

ML pipelines

Herramientas

Docker

Kubernetes

Descripción del empleo

Machine Learning Engineer, Senior Grid Dynamics Tarragona, tarragonés (comarca); provincia de tarragona; cataluña, Spain

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