About this role
Based in Zurich, Switzerland, the Applied AI Engineer position at ACTAI involves translating raw model capabilities into functional product features for everyday applications. This role suits professionals with a strong background in machine learning and systems engineering who want to build reliable, long-running AI workflows for end users.
Role Overview
The Applied AI Engineer bridges the gap between machine learning, underlying infrastructure, and user experience. Practitioners take ownership of complex problems from inception to production deployment, ensuring that models operate reliably without requiring intricate user prompting.
Key Responsibilities
Requirements
Skills and Tools
About the Company
ACTAI develops proactive applications designed to bring intelligence to everyday tasks like communication, organization, and workflows with minimal prompting. The organization focuses on reliable, long-running workflows with persistent context and real-world task completion to reduce hallucinations. Operating as a small team, the company makes collective decisions and moves rapidly while balancing quality and learning.
How to Apply
Candidates should apply directly on the official company careers page.
Skills and tech stack
Questions candidates ask
Where is the job located?
The position is located in Zurich, Switzerland.
What technologies and frameworks are used in this role?
The technology stack includes Python, PyTorch, JAX, large language models such as OpenAI-style APIs, LLaMA, and Qwen, inference and serving tools like vLLM, and vector databases.
What is the focus of the product team at ACTAI?
The team focuses on building proactive applications with minimal prompting, targeting high reliability for long-running workflows, persistent context, and real-world task completion.
What does the interview process look like?
The evaluation consists of three to four interviews conducted through virtual meetings and/or onsite sessions, handled by technical team members with an emphasis on transparency and prompt decisions.