Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting.
We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.
Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion.
We believe products will greatly reduce hallucinations.
Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things.
RoleAs an Applied AI Engineer, you will turn model capabilities into real product behavior.
You will own problems end-to-end, from shaping model behavior, to building the systems around it, to ensuring it performs reliably in production.
This role sits at the intersection of machine learning, systems, and product, focusing on making AI actually work for users, not just in demos, but in real-world usage.
FocusBuild and ship AI features end-to-end (model → system → user experience)Design and iterate on prompts, tools, memory, and agent workflowsTurn raw model outputs into structured, reliable, and predictable behaviorsDebug issues across the full stack (model, orchestration, infra, UX)Optimize for latency, cost, and production reliabilityDevelop lightweight evaluation frameworks to measure real-world performanceWork closely with product and engineering to translate ambiguous problems into working systems Tech StackPythonPyTorch / JAXLLMs (OpenAI-style APIs, LLaMA, Qwen, etc.
)Inference / serving (e.
vLLM)Vector DB Ideal ExperienceStrong foundation in machine learning and modern neural network architectures.
Hands-on experience with training, fine-tuning, or deploying ML modelsAbility to write clean, production-quality codeComfort working across abstraction layers (model → infra → product)Strong problem-solving.