Machine Learning Engineer, Senior

Grid Dynamics; Arbo; Presencial; EUR 70.000 - 90.000; Jornada completa

Descripción de la vacante

Grid Dynamics is seeking a Senior Machine Learning Engineer focusing on LLM systems and evaluation. You will own ML projects end-to-end, design evaluation methodologies, and build datasets and benchmarks to quantify model and product performance.

You will evaluate and improve LLM-based systems, including RAG applications and agents, while addressing safety concerns and translating results into actionable recommendations for product teams.

Formación

• 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.

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

Python

PyTorch

TensorFlow

JAX

RAG

LLM safety

Model evaluation

Communication

Descripción del empleo

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