We Don't Hire People Who Fit In. We Hire People Who Stand Out.
Replenit is building the autonomous decision layer for global retail. Other companies talk about AI. We ship it — to enterprise clients, at scale, every single day. If that sounds like your kind of fight, keep reading.
Why Replenit? Because “good enough” isn't in our vocabulary.
We replaced the entire journey-builder paradigm with an AI that makes millions of individualized decisions autonomously. No segments. No manual flows. Just outcomes. Here's what that means for you.
Outcomes over optics
We don't measure hours. We measure impact. If the model ships and revenue moves, you win.
Conviction over consensus
You don't need permission to ship here. You need evidence, a hypothesis, and the nerve to press deploy.
Clarity over complexity
Enterprise clients trust us because we simplify the impossible. That starts with how we think internally.
Speed over perfection
Perfect is the enemy of deployed. We iterate in production with real data, not in Figma with fake data.
Open Positions
Every role at Replenit is a force-multiplier. No seat-warmers, no busywork. If you're here, you're building.
Replenit is a fast-growing AI startup building autonomous decision systems. We help companies decide what should happen next for each customer — across replenishment, cross-sell, churn, and lifecycle decisions. We've proven the model. We're growing 50% QoQ. Now, we're ready to win France.
This isn't a “sit and wait for leads” role. You are the spearhead of our French expansion. As the first dedicated commercial hire for the French market, you will be the face of Replenit. You'll own the entire lifecycle: from leveraging your network to opening doors, to closing complex deals, and ensuring those clients thrive alongside our onboarding team.
Initially, you will be an individual contributor working directly with the founders. The goal? Build the blueprint for France, prove the repeatable motion, and then build the commercial team to scale it.
What you'll actually do
- Source & Close: Use your existing network and outbound expertise to land flagship French accounts
- Own the Full Cycle: From first touch to final contract negotiation
- Bridge the Gap: Partner closely with our Onboarding Specialists to ensure the “AI promise” becomes “Data reality” for your clients
- Localize the Strategy: Adapt our global value prop to the specific nuances of the French market
- Lead the Expansion: Act as the strategic lead for France, identifying which segments (Beauty, Fashion, CPG) to double down on
- Build the Future: Design the sales playbook that the next five hires will use in the market
Your success metrics
- New MRR / ARR: Direct impact on the bottom line in the French market
- Pipeline Velocity: How fast you can move a “Bonjour” to a “Signed”
- Market Penetration: Establishing Replenit as the go-to AI layer for French MarTech/Retail
Who this role is for
- The Entrepreneurial Seller: You don't need a manual; you write it
- The Networker: You know the players in the French SaaS/Digital ecosystem and your name carries weight
- The Scaler: You've sold before, but now you want to build the team you'd want to work for
- The Tech-Savvy Partner: You understand that selling AI requires more than a deck — it requires understanding a client's data maturity
Language requirement
- French: Native or C1+ (essential for navigating the French business landscape)
- English: Professional working proficiency (mandatory for internal collaboration)
What we're looking for
- 5+ years of experience in B2B SaaS sales (closing roles)
- Deep knowledge of MarTech, RetailTech, or Beauty Tech is a massive plus
- A history of hitting (and exceeding) quotas in a high-growth environment
- Can navigate a boardroom in Paris and a technical sync with our product team with equal ease
- Based in or near Paris (preferred) to be on the ground with clients
What's in it for you
- Competitive Base Salary: Benchmarked against top-tier EU AI startups
- Aggressive Commission Structure: Uncapped OTE with accelerators for over-performance
- ESOP Option: Real equity in one of the fastest-growing AI companies in Europe
- Founding Member Status: The opportunity to transition from individual contributor to building and leading the French commercial organization
Why this role matters: You aren't just hire #X — you are the architect of our international growth. In France, Replenit starts with you. You will define our reputation, land our most iconic logos, and eventually hire the team that sustains it all.
You will own the data onboarding and integration phase for new customers. Not by writing production code. Not by selling. By making sure integrations are implemented correctly, data flows reliably between systems, and Replenit can be activated without friction.
You will work directly with customer technical teams and act as the technical owner on our side. Initially, you will work closely with a founder. Over time, you will become the go-to authority for onboarding and data readiness.
What you'll actually do
- Work directly with customer teams during onboarding
- Own integration processes end-to-end — APIs, connectors, data pipelines, buckets
- Validate data ingestion and system-to-system transfers
- Approve payload structures, required fields, event schemas, and data consistency
- Identify issues early and drive resolution end-to-end
- Ensure decision events can be delivered and consumed reliably
- Translate real onboarding challenges into clear internal product feedback
- Help define and improve integration patterns and standards
- Push toward faster, cleaner, more scalable onboarding
What we're looking for
- 0–2 years of experience in integrations, data ingestion, or technical onboarding
- Understanding of REST APIs and JSON structures
- Ability to reason about data flows, system interactions, and event pipelines
- Detail-oriented and execution-focused
- Strong communication with technical stakeholders
- Ability to take ownership and drive topics to completion
Who this role is for
- You want real ownership, not task execution
- You like solving system-level problems, not isolated tickets
- You are curious about how data actually flows in production
- You want to build processes and systems, not just operate them
- You are comfortable in a high-growth startup environment (~50% QoQ)
Nice to have
- Experience with data warehouses (BigQuery, Snowflake, Databricks)
- Experience with event-based systems
- Experience with CDPs / CRM tools
- Experience working with international clients
- Based in Paris, France (preferred, not required)
- French: C1 minimum (preferred); English: professional working proficiency (mandatory)
We are looking for an AI Research Engineer to help design and prototype advanced AI capabilities that power our platform.
This role sits at the intersection of research and engineering. You will explore new approaches in machine learning and large language models, and translate promising ideas into practical systems that can operate at scale.
You will work closely with the Head of AI and ML engineers in a small and highly technical team, contributing to the long-term evolution of our AI capabilities.
What you'll work on
- Prototype and evaluate new machine learning and LLM-based approaches
- Design architectures for integrating LLMs into ML pipelines and data workflows
- Develop models for predictive and behavioral learning problems
- Experiment with representation learning, embeddings, and large-scale modeling
- Translate research ideas into working prototypes and production-ready components
- Stay up to date with recent developments in AI and applied ML
Tech Stack
- Python
- Databricks
- GitHub
- PyTorch / ML frameworks
- LLM tooling and embedding models
- Large-scale data and ML pipelines
What we're looking for
- MSc or PhD in Artificial Intelligence, Machine Learning, Computer Science, or related field
- Strong background in machine learning and deep learning
- Experience working with LLMs or transformer-based models
- Strong Python programming skills
- Ability to translate research ideas into practical implementations
Nice to have
- Experience with LLM systems, RAG pipelines, or embeddings
- Experience with recommendation systems or behavioral modeling
- Experience with multi-agent systems
- Experience working with large-scale datasets
- Publications or research experience in machine learning or AI
What we offer
- Opportunity to work on applied AI problems with real-world impact
- A small, highly technical AI team
- High ownership and autonomy
- Opportunity to shape the company's long-term AI direction
We are looking for a Machine Learning Engineer to join our AI team and help build and scale production-grade machine learning systems.
You will work on real-world ML problems involving large-scale behavioral and transactional data, contributing to models and pipelines that drive automated decision-making.
This role is highly hands-on and focuses on turning data into reliable AI systems — from engineering and experimentation to production pipelines.
You will work closely with the Head of AI in a small, highly technical team.
What you'll work on
- Build and maintain machine learning pipelines running on Databricks
- Design and implement feature engineering pipelines on large-scale datasets
- Develop and improve predictive models for customer and product behavior
- Support experimentation, evaluation, and model performance monitoring
- Contribute to data processing and ML infrastructure
- Collaborate on integrating ML and LLM-based components into production workflows
- Maintain clean, reliable code using GitHub-based development workflows
Tech Stack
- Machine learning frameworks (scikit-learn, LightGBM, PyTorch, etc.)
- Emerging LLM tooling and embeddings
- Python
- Spark / SQL
- Databricks
- GitHub
What we're looking for
- 1–3 years of experience in machine learning, data science, or ML engineering
- Strong Python programming skills
- Experience working with data pipelines or large datasets
- Familiarity with ML model development and evaluation
- Experience using Git-based workflows
- Comfortable working in a fast-moving startup environment
Nice to have
- Experience with Databricks or Spark
- Experience with recommendation or prediction systems
- Exposure to LLMs and foundational models
- Experience deploying ML pipelines in production
What we offer
- Work on real production AI systems
- A small and highly technical AI team
- High ownership and fast iteration cycles
- Opportunity to shape the next generation of AI-driven systems
We are looking for a DevOps Engineer to design, build, and maintain our cloud-native infrastructure on Google Cloud Platform. You will work closely with engineering, data, and ML teams to ensure reliable, scalable, and secure delivery of services and data pipelines.
Responsibilities
- Design and manage GKE clusters, Kubernetes networking, RBAC, and workload scheduling
- Build and maintain GitOps pipelines using ArgoCD and Helm charts
- Manage GCP networking: VPC, subnets, firewall rules, Cloud NAT, load balancers
- Operate and optimize Airflow and Databricks for big data / ETL pipelines
- Provide infrastructure and ops support for AI/ML pipelines, model training, and serving workloads
- Implement monitoring, alerting, and observability (Prometheus, Grafana, Cloud Logging)
- Automate infrastructure provisioning with Terraform and CI/CD pipelines
- Ensure security best practices: IAM, secret management, network policies, Cloud Armor
- Perform capacity planning, cost optimization, and incident response
Requirements
- Strong experience with Kubernetes, Helm, and container orchestration
- Hands-on experience with GCP services (GKE, IAM, VPC, Cloud SQL)
- Proficiency in GitOps workflows with ArgoCD or similar tools
- Experience operating Airflow and/or Databricks in production
- Familiarity with AI/ML pipeline infrastructure and MLOps practices
- Solid understanding of networking (TCP/IP, DNS, load balancing, firewalls)
- Infrastructure as Code experience (Terraform preferred)
- Scripting skills in Python and/or Bash
- Strong troubleshooting and incident management skills
Nice to have
- Experience with Strimzi / Kafka on Kubernetes
- Knowledge of service mesh (Istio) and API gateway patterns
- Contributions to open-source DevOps/infrastructure projects
- Experience with Kafka and RabbitMQ in production environments
What we offer
- Modern cloud-native stack with cutting-edge tools
- Collaborative engineering culture with autonomy
- Opportunity to work across infrastructure, data, and AI/ML domains
- Competitive compensation and benefits
- Remote-friendly work environment
Don't see your role? We still want to hear from you.
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