Applied Scientists — Machine Learning (MSc. or PhD)

Machine learning engineering for AI evaluation

Create, solve, review, and validate challenging tasks involving model development, training and inference systems, numerical computing, performance optimization, and Python. This is a coding-heavy role that requires practical experience using coding agents with Python.

A representative assignment may involve modifying a model, building a reproducible training or inference workflow, improving memory use or throughput, or debugging numerical and system failures. Implementations must meet objective correctness and performance requirements.

Responsibilities

• Develop and validate machine learning models, training pipelines, inference systems, and related infrastructure.
• Implement model components, data pipelines, evaluation systems, and numerical methods.
• Build reproducible workflows using Python and command-line tools.

Qualifications

• A master's degree or PhD in computer science, machine learning, AI, applied mathematics, statistics, engineering, or a related quantitative discipline.
• Strong professional or research experience in machine learning.
• Practical proficiency with Python and coding agents.

Equivalent tools may be considered where candidates demonstrate directly relevant depth. Experience at an established technology business, AI laboratory, research organization, or recognized engineering environment is strongly preferred; exceptional academic or open-source experience may also qualify.

Schedule and payment

This worldwide remote contract is approximately 15 hours per week. Scheduling is flexible, including weekends if desired.

The advertised range is $60 to $120 per hour, but actual compensation is per task meeting project specifications. Completion time varies by experience and workflow, and minimum submission requirements apply.

Selection and start

Selection includes screening questions, an AI interview of approximately 30 minutes, and hiring manager review. Immediate availability is preferred, with selected contributors expected to start their first task within 24 to 48 hours after onboarding.