Senior LLM Systems Engineer & Evaluation Lead

Grid Dynamics; Zug; Vor Ort; CHF 140.000 - 200.000; Vollzeit

Benefits dieser Stelle

Bleeding-edge projects

Motivated team

Competitive salary

Flexible schedule

Medical insurance

Sports

Corporate events

Professional development

Zusammenfassung

Grid Dynamics in Switzerland is seeking an exceptional Machine Learning Engineer to shape the next generation of AI systems, with a focus on modern LLM architectures, RAG implementations, autonomous agents and AI safety.

Working hours require overlap until 3 pm US EST. You will own ML projects from problem definition through implementation and collaborate with product, engineering, and research teams to translate business goals into measurable ML objectives.

Qualifikationen

• Strong Python programming and 5+ years in ML engineering.
• Experience with modern ML frameworks (PyTorch, TensorFlow, JAX).
• Experience training/fine-tuning ML models and working with LLMs.

Aufgaben

• Own ML projects from problem definition through implementation.
• Design evaluation methodologies for AI/ML systems.
• Create datasets, benchmarks, and metrics for model/product performance.
• Evaluate and improve LLM-based systems including RAG, agents, safety.
• Analyze model behavior, identify failure modes, and propose improvements.
• Build and maintain ML pipelines, tooling, and evaluation infra.
• Collaborate with product, engineering, and research teams to translate goals into ML objectives.
• Prototype and iterate rapidly to solve business challenges.

Kenntnisse

Python

PyTorch

TensorFlow

LLMs

RAG

MLOps

Evaluation

Datasets

Tools

PyTorch

TensorFlow

JAX

Python tooling

Jobbeschreibung

Grid Dynamics in Switzerland is seeking an exceptional Machine Learning Engineer to shape the next generation of AI systems, with a focus on modern LLM architectures, RAG implementations, autonomous agents and AI safety.

Working hours require overlap until 3 pm US EST. You will own ML projects from problem definition through implementation and collaborate with product, engineering, and research teams to translate business goals into measurable ML objectives.