The work
You will create, solve, review, and validate 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 failures, and optimizing memory use or throughput. Deliverables are assessed against objective correctness and performance requirements.
What the screening covers
The platform looks for strong professional or research experience in machine learning, supported by an MSc or PhD in a relevant quantitative discipline. Practical proficiency in Python is required, as is experience using coding agents in a Python workflow. The interview is likely to test whether you can explain a specific implementation, diagnose failures under follow-up, define reproducible validation, and distinguish a plausible result from a demonstrably correct one. 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.
Contract logistics
The role is global and fully remote, with a flexible schedule of about 15 hours per week. You may choose your working days and hours, including weekends. The process includes screening questions, an AI interview of about 30 minutes, and hiring manager review. Selected candidates are expected to begin their first task within 24 to 48 hours after onboarding.
Pay band: $60–120/hr
Compensation is output-based rather than time-based: payment is made for tasks that meet project specifications, task completion time varies, and minimum submission requirements apply.