Who we are
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Lemrock builds the infrastructure of agentic commerce.

Tomorrow, consumers won't just buy on e-commerce websites. They will discover, compare and purchase directly inside conversational AI interfaces like ChatGPT, Perplexity, and a growing long tail of specialized AI assistants.

For brands, this is a major shift: if they are not present in these environments, they become invisible.

Our mission: enable any brand to exist, sell and distribute in this new channel.

We raised a €7M seed round (the first in Europe on this market), with Jean-Baptiste Rudelle, founder of Criteo, on our board, and the Bpifrance DeepTech label.

Today, Lemrock is already:

* 100M conversations per month

* 150 brand clients

* a $100B market structuring at high speed

Context
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You'll join a tight-knit team with high product standards and **direct access to production deployment**: our models are tested in real-world conditions, with several million users each month, with very short cycles between prototyping, integration and deployment.

In this context, you'll work on the **next generation of agentic and recommendation systems**: building the pipelines, agents, and models that sit at the core of our product.

Your Mission
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Your goal is to **build and scale the agentic infrastructure that powers Lemrock's commerce intelligence**: turning raw signals into automated, self-improving systems at production scale.

Concretely, you will:

* **Analyze large-scale conversational interaction datasets** (100M events/month) to uncover behavioral patterns, intent signals, and performance drivers.

* **Design and deploy agentic pipelines end-to-end**, from data ingestion and enrichment to model orchestration, monitoring, and continuous improvement, integrated into systems exposed to millions of requests daily.

* **Build autonomous agents** that keep our knowledge infrastructure accurate and current.

* **Translate insights into iteration loops in production**, by updating, fine-tuning, and improving our existing recommendation and ranking algorithms with tight constraints on latency, robustness, and business outcomes.

* **Design and train new recommendation models from scratch** when needed, with a focus on scalability, evaluation rigor, and deployability in real-world traffic.

Candidate Profile
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* Strong academic background (MVA, ENS, X, Central...)

* 2 years of experience in AI, Agentic Systems, ML / Deep Learning, statistics or NLP

* Experience prototyping and deploying AI models into production

* Experience with production systems (APIs, monitoring, optimization)

* Strong interest in LLMs, recommendation and conversational systems: building agentic pipelines, agent orchestration, fine-tuning

* Comfortable with AI coding tools (Claude Code, Cursor)

* Thrives in ambiguity and 0-to-1 environments

* Fluent in English; French is a plus

Tech Stack
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### ML/AI (required)

* Agentic frameworks (e.g., Langchain) and/or native SDKs like OpenAI/Anthropic

* Observability and evaluation for LLM/agent workflows

* Vector search, scoring, prompting, LLM orchestration

* Hybrid recommender systems, causal inference, probabilistic models

### Bonus (appreciated but not required)

* TypeScript

* Engineering: Docker, GCP, PostgreSQL, Redis, Vector DBs, PostHog

* CI/CD

Why Join Us
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* Join a company in **early breakout mode**, already generating revenue, with a clear technological edge and strong financial backing to fuel its growth

* Work directly with an **experienced team**: two repeat YC founders who've scaled product \& tech before, a former strategy/innovation director (10 years in the sector), and ex-strategy consultants (McKinsey QuantumBlack, BCG)

* Be part of the **next structural shift of the web**: agentic interfaces

* Own **mission-critical topics**, grow fast, and shape the company's future trajectory

Package
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Competitive salary disclosed during the interview process.