Senior ML Engineer, Agent-Native AI Search

Jobgether; France; On-site; EUR 90,000 - 130,000; Full time

Benefits offered by this job

Competitive pay

Career growth

Flexible work

Innovative culture

Impactful AI projects

International team

Job summary

Jobgether, on behalf of its partner, is seeking a Senior ML Engineer (Agentic Search) based in France. You will design, train, and deploy models powering retrieval, ranking, and indexing at scale for a real-time information access platform.

You will work across ML and software engineering, own architectural decisions, and collaborate with distributed teams to improve latency, quality, and reliability in production systems.

Qualifications

• At least 5 years of experience in software engineering or applied ML.
• Proven experience deploying and operating ML models in production systems.
• Hands-on experience with retrieval, ranking, or related ML applications.
• Strong understanding of ML and modern deep learning techniques.
• Experience with large-scale data systems and high-throughput environments.

Responsibilities

• Design, train, deploy, and maintain ML models for retrieval, reranking, and search relevance in production environments.
• Build and optimize embedding-based indexing and large-scale information retrieval systems.
• Develop ML models supporting web crawling, data selection, and content understanding.
• Define meaningful quality metrics for agent-native search and build evaluation pipelines.
• Collaborate with engineering teams to integrate ML models into production services.
• Research, experiment with, and apply state-of-the-art techniques in search and LLM-integrated systems.

Skills

Analytical thinking

Problem solving

Ownership

Product mindset

Tools

Python

Go

C++

Job description

Jobgether, on behalf of its partner, is seeking a Senior ML Engineer (Agentic Search) based in France. You will design, train, and deploy models powering retrieval, ranking, and indexing at scale for a real-time information access platform.

You will work across ML and software engineering, own architectural decisions, and collaborate with distributed teams to improve latency, quality, and reliability in production systems.