Machine Learning Engineer

$60–90/hr

61 people hired

RemoteGlobalTechnology0-1 YearContractPosted Aug 11, 2026

About this role

Machine Learning Engineer responsible for designing complex, multi-step ML evaluation tasks and conducting end-to-end experiments to benchmark frontier AI models. The role involves translating open-ended ML research ideas into reproducible tasks, implementing model or training changes, running experiments, analyzing results, and identifying where advanced AI models fail. You will work closely with AI researchers to create rigorous ground-truth solutions and agentic evaluation benchmarks across machine learning and reinforcement learning workflows.

Responsibilities

Design complex multi-step machine learning evaluation tasks.
Translate ML research ideas into reproducible experiments.
Implement model and training changes using Python.
Run end-to-end training experiments and analyze results.
Develop tasks involving reinforcement learning, reward functions, and training behavior.
Evaluate frontier model performance and identify reasoning or implementation failures.
Produce ground-truth solutions and benchmark results.
Collaborate with researchers to improve task consistency and evaluation quality.

Qualifications

MSc or PhD in Machine Learning, Computer Science, STEM, or equivalent research-intensive experience.
1+ years of research or research-engineering experience.
Hands-on experience training and evaluating ML models end-to-end.
Strong knowledge of LLM capabilities, limitations, and evaluation techniques.
Working proficiency in Python and Git, including scripting and notebook workflows.
Understanding of reinforcement learning concepts such as reward functions and policy training preferred.
AI training, benchmark authoring, or model evaluation experience preferred.
Strong analytical thinking, technical writing, attention to detail, and ability to solve ambiguous research problems independently.
Must be US-based and available approximately 35 hours per week.