Applied Artificial Intelligence Engineer

Join our team as an Applied Artificial Intelligence Engineer, where you'll work closely with senior engineers, product management, and our Chief Product & Technology Officer. This hands-on role focuses on building production-grade AI-powered workflows and infrastructure. You'll own the evaluation and measurement of the features you ship, collaborate with site reliability engineering to build the necessary infrastructure, and ensure the reliability, cost, and safety of our AI layer. Additionally, you'll treat prompts and model configurations as code, contribute to our quarterly AI roadmap, and actively participate in design discussions and code reviews.

• Concevoir, prototyper et expédier rapidement des capacités alimentées par l'IA.
• Construire des flux de travail agentiques et des modèles d'orchestration, en contribuant à la conception et en possédant l'implémentation.
• Posséder le harnais d'évaluation pour les fonctionnalités que vous expédiez, construire les ensembles de données, la notation automatisée.

• This is a great fit if you've shipped LLM features in production, you're comfortable when the answer involves a Terraform plan rather than a prompt tweak, and you're ready to go deeper - with senior engineers around you to learn from
• You are able to work collaboratively and independently
• You have excellent oral and written communication skills
• You've written and applied infrastructure as code yourself. Terraform preferred - you've authored modules, read a plan carefully before applying it, and recovered from state that didn't match reality. Terragrunt, CloudFormation, or CDK experience transfers
• You default to AI in your own workflow. You've already integrated modern AI tools into how you work, and you use them to move faster rather than as a crutch
• You bring intellectual humility, empathy, and strong listening skills to the team
• You have familiarity with RAG architectures and vector stores, and an interest in multi-agent orchestration frameworks - you don't need to have run them at scale, but you should know the landscape
• You can reason about what an AI feature costs to run - tokens, context size, retries, caching - and you treat that as an engineering constraint rather than someone else's problem
• You prioritize a "question first, then answer" approach, thinking critically about problems before committing to a code path
• You hold a Bachelor's degree (or higher) in Computer Science, Statistics, Mathematics, or Engineering, or equivalent practical experience
• You are comfortable with agile development
• You've taken LLM work past a prototype at least once - you've thought about output quality, iterated on prompts against real usage, and dealt with the gap between "works in the notebook" and "works for customers."
• You've built evaluation beyond trial and error - you can measure accuracy, safety, and latency, and you know when a benchmark win is real and when it's noise
• Strong fullstack experience - Ruby on Rails (or any other backend language/framework) and Vue.js/React (or any other framework) preferred. You're comfortable in a production SaaS codebase and can navigate unfamiliar systems without needing everything explained first
• 3+ years of professional software engineering experience, including hands-on work with AI/ML or LLM-powered features
• Working knowledge of AWS beyond the console: IAM (roles, policies, least privilege), VPC and networking basics, secrets management, and how a containerized workload gets deployed and observed. Kubernetes familiarity is a strong plus
• You understand how to structure LLM calls for reliability: using function calling and structured outputs to ground results, testing responses systematically, and designing for failure modes (hallucinations, latency, cost)
• You've built something agentic - an LLM reasoning across multiple steps, making decisions from tool outputs, adapting to feedback. Side projects count
• You've built an eval harness that changed a decision - where the numbers told you to ship, hold, or roll back, and you listened
• You've operated AI in production - you've been on the other side of a cost spike, a provider outage, a model deprecation, or a quality regression that only showed up under real traffic
• You have a bias toward simplicity - you're pleased when a well-structured baseline beats something more complex, and you know when not to reach for a model at all
• You've worked with Amazon Bedrock, or have opinions about model routing and provider abstraction earned the hard way
• You've worked within the email ecosystem or a similar high-scale communication platform
• You've built with AI coding agents and have a view on what they're good and bad at - we use them, and we're still figuring out the right practices
• You prototype fast, iterate quickly, and use data to change direction when the evidence says so
• You share what you learn with peers - writeups, demos, docs, internal talks