Machine Learning Engineer, Senior

Grid Dynamics; Badajoz; Presencial; EUR 55.000 - 90.000; Jornada completa

Descripción de la vacante

Grid Dynamics is seeking a Senior Machine Learning Engineer in Badajoz to lead LLM-based system design, evaluation, and RAG implementations. You will own end-to-end ML projects, develop evaluation frameworks, and collaborate across product, engineering, and research teams to translate business goals into measurable ML objectives.

The role emphasizes building scalable ML pipelines, tooling, and safe, robust AI products, with a focus on practical improvements and clear communication of results.

Formación

• 5+ years of experience in machine learning engineering or related field.
• Strong knowledge of ML fundamentals and model evaluation.
• Proficient Python programming with PyTorch, TensorFlow, or JAX.
• Experience with Large Language Models beyond API integration.
• Ability to design and implement ML pipelines and evaluation infrastructure.

Responsabilidades

• Own ML projects from problem definition through implementation.
• Design and implement evaluation methodologies for ML/AI systems.
• Create datasets, benchmarks, and metrics to measure performance.
• Evaluate and improve LLM-based systems including RAG and agents.
• Build ML pipelines, tooling, and evaluation infrastructure.
• Collaborate with product, eng, and research teams to translate goals into ML objectives.
• Prototype rapidly to solve business and product challenges.
• Communicate findings and trade-offs to technical and non-technical stakeholders.

Conocimientos

Python

ML fundamentals

LLM concepts

Model evaluation

ML pipelines

Communication

Herramientas

PyTorch

TensorFlow

JAX

Descripción del empleo

Machine Learning Engineer, Senior Grid Dynamics Badajoz, tierra de badajoz (comarca); provincia de badajoz; extremadura, 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.