What the work actually is
This is backend engineering in service of AI research, not annotation or data labelling. You sit next to ML engineers and build the services they hit: endpoints that kick off training jobs, pull evaluation results, expose benchmark runs, and stream logs back. Day to day that means writing production Python, designing RESTful interfaces and owning them through deployment, handling asynchronous workloads, and debugging why a pipeline that worked at ten requests falls over at ten thousand. Review work is explicitly part of the job — you are expected to read other people's PRs, give substantive feedback, and merge.
What the platform screens for
Turing's process here is short and concrete: a 60-minute technical interview with a live Python coding challenge. Before that, expect verification of the stated bar — 8+ years professional Python, hands-on API lifecycle ownership, async programming, testing and CI/CD discipline, Git, and a CS or engineering degree. NumPy and Pandas familiarity is required; PyTorch or TensorFlow exposure is a plus rather than a gate. Screeners tend to push on specifics: which async framework, what the concurrency model was, how you measured the performance problem before you fixed it. Vague seniority claims unravel quickly under that kind of follow-up.
Logistics
- Fully remote contractor assignment, no medical or paid leave.
- Minimum 4 hours per day and 20 hours per week, with at least 4 hours overlapping Pacific time.
- Initial contract duration of 3 months, adjustable depending on the engagement.
- Eligible locations as listed: India, Pakistan, Nigeria, Kenya, Egypt, Ghana, Bangladesh, Turkey, Brazil, Mexico.
Pay is undisclosed on this listing. Turing typically sets hourly rates by seniority and location band during the offer conversation; treat any figure you see quoted secondhand as unverified.