About this role
Bjakcareer is hiring a Member of Technical Staff, Machine Learning in China to develop core machine learning components for proactive, AI-native applications. This position is suitable for engineers who want to learn how large-scale machine learning behaves in production environments outside of research settings.
Role Overview
Bjakcareer seeks a Member of Technical Staff, Machine Learning to work in China on real production systems from day one. The professional in this role will build core machine learning components, fine-tune models, and manage data pipelines while operating under strict production constraints such as latency, cost, reliability, and safety.
Key Responsibilities
Requirements
Skills and Tools
About the Company
ACTAI builds proactive applications for users globally to make everyday software like email, notes, tasks, calendar, and workflows AI-native with minimal to no prompting. The company focuses on persistent context, real-world task completion, and high reliability for long-running workflows to minimize hallucinations.
How to Apply
Interested candidates should apply on the official company careers page.
Skills and tech stack
Questions candidates ask
What is the location of the Member of Technical Staff, Machine Learning role?
The position is located in China.
What technical stack is used in this position?
The tech stack includes Python, PyTorch or JAX, and production machine learning systems running on GPUs.
What does the interview process look like?
The interview process consists of 3 to 4 interviews conducted via virtual meetings and/or onsite, evaluated by technical team members.
What are the primary responsibilities for this job?
Responsibilities include building and improving machine learning components, fine-tuning models, implementing testing and evaluation, managing data pipelines, debugging production issues, and shipping iterative updates.
What kind of experience is required?
Candidates need strong foundations in machine learning and modern neural architectures, along with hands-on experience in training, fine-tuning, or deploying models.