Senior Machine Learning Engineer

Grid Dynamics; Mos; Presencial; EUR 70.000 - 110.000; 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, design evaluation frameworks, and build datasets, benchmarks, and metrics to measure model performance.

The role involves evaluating LLM-based systems, including RAG applications and safety-focused components, and collaborating with product, engineering, and research teams to translate business goals into measurable ML objectives.

Formación

• 5+ years of experience in ML engineering or related field.
• Strong understanding of ML 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 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 ML projects from problem definition through implementation.
• Design and implement evaluation methodologies for AI/ML systems.
• Create datasets, benchmarks, and metrics to measure model 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 fundamentals

Model evaluation

LLM systems

RAG concepts

Communication

Publications

Herramientas

PyTorch

TensorFlow

JAX

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

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