(Senior) Machine Learning Engineer

Grid Dynamics; Santander; Presencial; EUR 90.000 - 130.000; Jornada completa

Descripción de la vacante

Grid Dynamics is seeking a Senior Machine Learning Engineer focused on LLM systems and evaluation to join our AI research team in Santander. You will own ML projects end-to-end, design robust evaluation methods, and shape next-generation AI products.

Strong Python, PyTorch, and ML Ops skills are essential, with experience in RAG, agents, and safety considerations. You will collaborate with data scientists and engineers to build scalable pipelines, translate business goals into measurable ML

Formación

• ~5+ years of experience in Machine Learning Engineering or related field.
• ~ Strong understanding of machine learning fundamentals and model evaluation
• ~ Strong Python programming skills and experience with PyTorch, TensorFlow, or JAX
• ~ Understanding of retrieval-augmented generation (RAG), agentic systems, and LLM safety concepts
• ~ Experience training, fine-tuning, or adapting ML 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

Responsabilidades

• Own machine learning 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.
• 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

Machine Learning Engineering

Python programming

ML frameworks

Model evaluation

RAG concepts

LLM safety

Data pipelines

Communication

Herramientas

PyTorch

TensorFlow

JAX

Cloud platforms

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