
Member of Technical Staff, Machine Learning
About this role
ACTAI is hiring a Member of Technical Staff, Machine Learning in the United States to develop core machine learning components for production systems. This position suits engineers who want to build strong systems judgment by shipping, debugging, and iterating on real-world machine learning applications.
Role Overview
ACTAI seeks a Member of Technical Staff, Machine Learning to work on production systems from day one. The professional will construct core machine learning components, fine-tune models, and manage real-world constraints such as cost, reliability, latency, and safety. The organization focuses on building proactive applications that bring intelligence to everyday workflows with minimal prompting.
Key Responsibilities
Requirements
Skills and Tools
About the Company
ACTAI builds proactive applications designed to bring intelligence to everyday tasks, organizing, and workflows with minimal to no prompting. The company focuses on persistent context, long-running workflow reliability, and real-world task completion to reduce hallucinations and help users spend time on valuable activities. Operating as a small, hands-on, and high-talent-density team, ACTAI makes decisions collectively and moves at rapid speed.
How to Apply
Interested candidates should apply on the official company careers page.
Skills and tech stack
Questions candidates ask
Where is the Member of Technical Staff, Machine Learning role located?
The position is located in the United States.
What technical stack is used in this role?
The technical stack includes Python, PyTorch or JAX, and production machine learning systems running on GPUs.
What is the primary focus of the machine learning work at ACTAI?
The focus involves building and improving machine learning components across data, training, evaluation, and inference, fine-tuning models, building data pipelines, and debugging production issues under real constraints like latency, cost, reliability, and safety.
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.