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
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 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.
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
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
TypeScript
Engineering: Docker, GCP, PostgreSQL, Redis, Vector DBs, PostHog
CI/CD
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
Competitive salary disclosed during the interview process.
Develop computer vision and deep learning algorithms for centimeter-level vehicle localization on HD maps, combining surround-camera systems, 3D geometry, and optimization for robust autonomous driving.
Build and ship multi-step agentic systems on Roku’s streaming platform, combining distributed systems, telemetry, device constraints, and internal tools to improve engineering workflows at scale.
Build and maintain AI-powered features and services, from agent infrastructure and workflow orchestration to production monitoring, while improving code quality and delivering reliable intelligent collaboration tools.
Train and evaluate PyTorch computer vision models for visual localization, build large-scale image and 3D data pipelines, and help improve benchmarks and production inference for precise positioning.
Own the full lifecycle of internal AI tools, from identifying workflow bottlenecks to building, rolling out, and improving systems that reduce manual work and help teams use AI effectively.
