ML Engineer (MSc. or PhD)

$60–120/hr

rate as listed

Workload: ~15 hrs/week
Engagement: Project-based
Location: Worldwide
Field: STEM and Coding

Develop and assess challenging machine-learning implementations spanning models, training pipelines, inference systems, and performance optimization. This remote contractor role requires an MSc or PhD, substantial machine-learning experience, Python proficiency, and experience using coding agents.

The work

You will create, solve, review, and validate coding-heavy machine-learning tasks. Day-to-day work may include implementing model components, building data or evaluation pipelines, creating reproducible training and inference workflows, debugging numerical or system-level failures, and improving memory use or throughput. Deliverables must meet objective correctness and performance requirements.

What the screening covers

The platform screens for professional or research depth in machine learning, including the ability to explain specific design decisions and diagnose failures under follow-up. Python proficiency is required. Experience using coding agents with Python in your workflow is also required, and candidates should be prepared to explain how they verify agent-generated code rather than accepting it without review. Experience in an established technology company, AI laboratory, research organization, or recognized engineering environment is strongly preferred, while exceptional academic or open-source work may also qualify.

Logistics and compensation

This is a global, fully remote contractor role with a flexible, largely asynchronous schedule of about 15 hours per week. Work may be completed on weekdays or weekends, but selected candidates are expected to begin their first task within 24 to 48 hours after onboarding. Compensation is output-based and applies only to tasks that meet project specifications, with minimum submission requirements. Pay band: $60–120/hr. This is an observed band, not a guaranteed hourly payment.

What they look for

Machine Learning

Included with this listing

Interview prep — 10 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: This verifies the required graduate degree and whether the candidate can connect specific quantitative training to model implementation, numerical methods, or experimental work.
A strong answer: Name the institution, degree, discipline, and completion status, then identify a thesis, publication, or advanced project with direct relevance to machine learning. Clarify the technical contribution you personally made.
Where people lose it: Candidates lose points by naming a degree without its field or by describing a group research project without separating their own work from the team's output.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.

STEM AND CODING on Sidequest

What stem and coding AI work pays

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