Senior Machine Learning Engineer - Grid Dynamics

Grid Dynamics; País Vasco; Presencial; EUR 70.000 - 100.000; Jornada completa

Descripción de la vacante

Grid Dynamics seeks a Senior Machine Learning Engineer to lead LLM systems and evaluation efforts, focusing on AI safety, RAG applications, and agent-based components. You will define problem statements, design benchmarks, and collaborate with cross-functional teams to translate business goals into measurable ML objectives.

Responsibilities include building pipelines, evaluating AI systems, and iterating rapidly to deliver robust ML products.

Formación

• 5+ years of experience in ML engineering or related field.
• Strong Python and ML fundamentals with model evaluation.
• Experience with LLMs and RAG, agents, and safety concepts.

Responsabilidades

• Own ML projects from problem definition through implementation.
• Design evaluation methodologies for AI/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

Python

PyTorch

TensorFlow

JAX

RAG systems

LLM safety concepts

Model evaluation

Communication skills

Herramientas

Hugging Face

ML pipelines tooling

DVC

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