Senior Machine Learning Engineer

Grid Dynamics; España; On-site; PHP 1,800,000 - 2,600,000; Full time

Job summary

Grid Dynamics seeks a Senior Machine Learning Engineer focused on LLM systems and evaluation. You will own ML projects from problem definition through implementation and collaborate with product, engineering, and research teams.

You will design evaluation methodologies, build datasets/benchmarks, and iterate to translate business goals into measurable ML objectives. Strong Python and ML framework experience (PyTorch, TensorFlow, or JAX) are essential.

Qualifications

• 5+ years in Machine Learning Engineering or related field.
• Strong understanding of ML fundamentals and model evaluation.
• Experience with PyTorch, TensorFlow, or JAX.
• Experience with LLMs, RAG, and safety concepts is desirable.
• Ability to work in ambiguous problem spaces with limited data.

Responsibilities

• Own machine learning projects from problem definition through implementation.
• Design and implement evaluation methodologies for AI systems.
• Create datasets, benchmarks, and metrics to measure model performance.
• Evaluate and improve LLM-based systems including RAG apps and agents.
• Build and maintain ML pipelines, tooling, and infrastructure.
• Collaborate with product, engineering, and research teams to translate goals into ML objectives.
• Prototype rapidly to solve business and product challenges.

Skills

Python programming

ML fundamentals

Model evaluation

Strong communication

Ambiguous problem solving

Tools

PyTorch

TensorFlow

JAX

Job description

Descripción del trabajo

We are looking for a talented Senior Machine Learning Engineer - LLM Systems & 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