Senior ML Engineer – Hybrid (NLP & Large-Scale Inference)

Startupvalleys; Vaud; Hybrid; CHF 120,000 - 180,000; Full time

Job summary

Giotto.ai, a Swiss AI research lab, is seeking a mid‑senior to senior Machine Learning Engineer to design, build, and deploy advanced AI systems focused on reasoning and scalable inference in Lausanne. This full‑time role offers a hybrid work model in Vaud.

You will work on large‑scale ML systems, distributed training pipelines, and next‑generation architectures for reasoning models, collaborating closely with our research and engineering teams.

Qualifications

• Mid-senior or senior Machine Learning Engineer with at least three years of experience in NLP or machine learning.
• Strong programming skills in Python and hands-on experience with PyTorch.
• Experience building or deploying machine learning systems in production.
• Familiarity with LLMs and transformer architectures.
• Experience with distributed training or inference systems.
• Comfortable working in a fast-moving environment that brings together research and production engineering.

Responsibilities

• Design, build, and deploy advanced AI systems focused on reasoning and scalable inference.
• Work on large-scale machine learning systems, distributed training pipelines, and next-generation architectures for reasoning models.
• Collaborate closely with our research and engineering teams.
• Depending on experience, lead a feature squad or project team.

Skills

Python

PyTorch

Hugging Face ecosystem

Large Language Models (LLMs)

Distributed computing

vLLM

Ray

Education

PhD in mathematics, physics, computer science, or another quantitative scientific discipline

Tools

Docker

Kubernetes

Job description

Giotto.ai, a Swiss AI research lab, is seeking a mid‑senior to senior Machine Learning Engineer to design, build, and deploy advanced AI systems focused on reasoning and scalable inference in Lausanne. This full‑time role offers a hybrid work model in Vaud.

You will work on large‑scale ML systems, distributed training pipelines, and next‑generation architectures for reasoning models, collaborating closely with our research and engineering teams.