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Great Value Hiring

Machine Learning Engineer (AI Coding Agent Evaluation)

Great Value Hiring

United Kingdom · 合同

抢先申请

经验
2年以上
薪水
USD 85 – USD 85 / hour
职位空缺
1
发布
3小时前
工作模式
在办公室
合格
Open to all qualified candidates regardless of legally protected characteristics. Reasonable accommodations are available upon request.
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职位描述

Overview

This contract role involves assessing and enhancing advanced AI coding models through detailed technical reviews. The focus lies on genuine machine learning engineering tasks and robust model evaluation processes.

Key Responsibilities

  • Utilize cutting-edge AI coding agents to execute and appraise intricate machine learning and AI engineering tasks.
  • Evaluate implementations generated by models covering training procedures, inference pipelines, MLOps frameworks, and large language model (LLM) applications.
  • Detect bugs, corner cases, performance bottlenecks, and operational failures within model outputs.
  • Conduct comparative analyses across outputs from several frontier AI models to identify strengths and shortfalls.
  • Employ sound engineering judgment in addressing realistic ML engineering challenges.

Time Commitment

Assignments operate in sprint sessions lasting between 12 to 24 hours, depending on client needs.

Candidate Profile

  • At least two years of professional experience in machine learning engineering.
  • Proven background in constructing production-level ML systems, deployment infrastructures, applications involving LLMs, or AI-driven products.
  • Frequent use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or their equivalents.
  • Capability to critically assess machine learning implementations generated by AI and understand technical trade-offs involved.
  • Preferred experience includes deploying machine learning systems into production environments.
  • The role encourages applications from all qualified individuals and offers reasonable accommodations as needed.

Compensation Details

  • Payment amounts to $400 for each approved task.
  • Typical task completion spans approximately 2 to 3 hours after initial ramp-up.
  • Remuneration is strictly tied to work that meets acceptance criteria.

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