The work
You will create and assess challenging machine learning engineering tasks. Day-to-day work may include implementing or modifying models, building data and evaluation pipelines, constructing reproducible training or inference workflows, debugging numerical or system-level failures, and optimizing memory use or throughput. You will also verify implementations against objective correctness and performance requirements.
What the screen covers
The platform screens for a master’s degree or PhD in a relevant quantitative discipline, substantive professional or research experience in machine learning, and practical proficiency with Python and coding agents. Expect follow-up questions about specific systems you built, failures you diagnosed, performance claims you measured, and how you distinguish a correct implementation from one that merely runs. Experience in an established technology company, AI laboratory, research organization, or recognized engineering environment is strongly preferred, while exceptional open-source or academic experience may also qualify.
Logistics
This is a global, fully remote contractor role with a flexible schedule of about 15 hours per week. You choose your working days and hours, including weekends if desired, so the task-based workflow can be handled asynchronously. The process includes screening questions, an AI interview of approximately 30 minutes, and hiring manager review. Candidates should be ready to begin immediately and, if selected, start the first task within 24 to 48 hours after onboarding.
Compensation
Pay band: $60–120/hr
Compensation is output-based, with payment for tasks that meet project specifications rather than time spent alone. Task completion time can vary by experience and workflow, and minimum submission requirements apply.