ML Engineer - AI Search & Ranking (Remote-friendly)

Linkup; Paris; À distance; EUR 90 000 - 150 000; Plein temps

Avantages offerts par ce poste

Flexible remote-work policy

Résumé du poste

Linkup, the web search engine built for AI, is seeking a Machine Learning Engineer to work with the CTO and tech team in Paris. You will design, train and ship ranking and retrieval models for AI agents, and push signals beyond relevance while optimizing latency and GPU costs.

You will build robust evaluation pipelines and translate feedback into measurable ranking improvements. You should thrive in a fast-paced startup, delivering strong, data-backed search quality improvements and

Qualifications

• Strong hands-on experience in information retrieval, ranking or search in production.
• Build rigorous evals and validate data behind every number.
• Care about latency, cost and reliability as much as model quality.
• Thrive in a fast-paced, high-ownership startup environment.

Responsabilités

• Design, train and ship ranking and retrieval models for AI agents.
• Bring new ranking signals into the engine, beyond pure relevance.
• Build the evaluation framework that tells us what really improves search quality.
• Optimize model serving for latency and GPU cost.
• Turn customer and benchmark feedback into ranking improvements, with clear before/after numbers.

Connaissances

Info retrieval / ranking production

Eval frameworks

Latency & cost focus

Startup ownership

Outils

Python

PyTorch

Hugging Face

vLLM

Ray

Spark / Databricks

Vespa

Kubernetes

GitHub Actions

Terraform

Description du poste

Linkup, the web search engine built for AI, is seeking a Machine Learning Engineer to work with the CTO and tech team in Paris. You will design, train and ship ranking and retrieval models for AI agents, and push signals beyond relevance while optimizing latency and GPU costs.

You will build robust evaluation pipelines and translate feedback into measurable ranking improvements. You should thrive in a fast-paced startup, delivering strong, data-backed search quality improvements and