ML Developer Experience Engineer

About the role

As an ML Developer Experience Engineer, you will empower ML engineers and developer teams to leverage reinforcement learning without needing deep RL expertise. You’ll build developer-centric tooling, abstractions, and workflows that make training, evaluating, and deploying RL models intuitive, robust, and scalable — fueling our customers’ ability to adopt, expand, and own RL use cases.

This role is core to our land-and-expand strategy: your work will enable customers to scale independently, reducing friction and unlocking self-service adoption across organizations.

You will collaborate closely with Technical Success, Product, and Engineering team to identify patterns, extract reusable solutions, and shape the boundary between tooling and product.

This role is based in Paris or remote within EMEA.

Your Responsibilities

Developer Tooling & API Abstractions

• Design and build intuitive SDKs, libraries, APIs, and tooling that make RL workflows accessible and productive for developers and ML engineers;

• Balance simplicity and flexibility — support common patterns while enabling advanced configurations and extensibility;

• Partner with internal teams to refine primitives into documented, reusable modules that accelerate customer success.

Customer-Centric Developer Experience

• Work with across tech team and customer succes to identify recurring customer patterns and workflow bottlenecks;

• Translate feedback into tooling improvements, error messaging, onboarding flows, and reference examples;

• Own comprehensive documentation, examples, and tutorials that make complex concepts clear and approachable.

Cross-Functional Bridge & Quality Standards

• Act as the translator between customer needs and product evolution — help shape internal libraries into long-term platform capabilities;

• Ensure high quality through tests, edge-case handling, and reliability as a core tenet of the developer experience;

• Collaborate with Product to influence roadmap decisions with real usage and pain-point insights.

Your (ideal) background

We value signal, judgment, and mindset over perfect resumes. Strong candidates often display:

• Experience building developer-facing libraries, tooling, SDKs, APIs, or frameworks used in production by other developers or teams;

• Comfort with Python (mandatory) and/or other languages common in the ML ecosystem

• Track record of elevating developer productivity and satisfaction through documentation, design, and feedback loops;

• Understanding of ML workflows, training loops, evaluation, and deployment patterns — RL familiarity is a plus, not a prerequisite;

• Empathy for developers as users — you understand that clarity and usability drive adoption;

• Strong collaborative skills and ability to influence across technical and customer-facing teams.

Nice to have (not blockers):

• Contributions to open-source tooling or developer libraries;

• Experience with distributed training, PyTorch/JAX, or developer platform tools (e.g., language server integrations, CLI frameworks, documentation systems);

• Experience shaping developer experience strategy in a product organization

• Strong interest in Rust