About this role
This position suits a pragmatic engineer with a strong foundation in machine learning and systems who wants to build reliable production applications. The Applied AI Engineer role is based in Spain at bjakcareer and focuses on transforming model capabilities into real-world product behavior.
Role Overview
The Applied AI Engineer sits at the intersection of machine learning, systems, and product development. The professional in this position will take ownership of problems from start to finish, shaping model behavior, building supporting systems, and guaranteeing reliable performance in live production environments.
Key Responsibilities
Requirements
Skills and Tools
About the Company
ACTAI builds proactive applications designed for users globally who do not utilize complex prompting, focusing on basic applications like email, notes, tasks, calendar, conversations, errands, organizing, and workflows. The organization emphasizes high reliability for long-running workflows, persistent context, and real-world task completion to reduce hallucinations and help people dedicate time to valuable activities. Operating in small teams, the company values collective decision-making, speed, high-quality output, and independent execution.
How to Apply
Interested candidates should visit the official company careers page to submit an application for this opportunity.
Skills and tech stack
Questions candidates ask
Where is the Applied AI Engineer position located?
The role is located in Spain.
What technical stack is required for this position?
The tech stack includes Python, PyTorch or JAX, Large Language Models like OpenAI-style APIs, LLaMA, and Qwen, inference and serving tools like vLLM, and vector databases.
What is the interview process like?
The interview process consists of three to four interviews conducted via virtual meetings or onsite, evaluated by technical team members.
What is the primary focus of the role?
The focus is on building and shipping end-to-end AI features, designing prompts and agent workflows, optimizing for latency and reliability, and turning raw model outputs into structured behaviors.