About this role
Lead Engineer, Machine Learning at bjakcareer in the United States is a hands-on leadership role that oversees the execution layer of intelligence to convert research and model capabilities into production systems. This position suits an experienced machine learning professional who wants to operate at the intersection of research, systems, and product development.
Role Overview
The Lead Engineer, Machine Learning role at bjakcareer is based in the United States and focuses on building proactive applications with minimal prompting for users globally. The position involves owning the end-to-end machine learning systems powering the organization, covering data management, training, evaluation, inference, and deployment.
Key Responsibilities
Requirements
Skills and Tools
About the Company
bjakcareer operates as a small, hands-on team with high talent density where engineers maintain broad ownership. The organization focuses on building proactive applications for global users to bring intelligence to conversations, errands, organizing, and workflows without complex prompting.
How to Apply
Interested candidates should apply on the official company careers page.
Skills and tech stack
Questions candidates ask
What is the primary focus of the Lead Engineer, Machine Learning role?
The role owns the execution layer of intelligence, turning research and model capabilities into reliable, scalable production systems across data, training, evaluation, inference, and deployment.
Where is the position located?
The job is located in the United States.
What technical stack is used in this role?
The tech stack includes Python, PyTorch, JAX, and GPU-based training and inference systems.
What does the interview process look like?
The process consists of between 3 and 4 interviews conducted via virtual meetings and/or onsite.
What type of experience is required for this position?
Candidates must have experience building and shipping machine learning systems used in production, strong software engineering and systems fundamentals, and experience operating machine learning workloads at scale, particularly on GPU-based systems.