ML Engineer
Hire FeedAbout the role
Role: ML Engineer (Remote)
Location: Remote (Work from Anywhere)
Job Type: Contract
Payout: $400 per accepted task
Role Overview:
We are hiring for one of our clients to support a frontier code agents project through structured technical assessments. The work focuses on realistic machine learning engineering workflows and model evaluation.
Key Responsibilities:
- Use frontier AI coding agents to complete and evaluate complex machine learning and AI engineering tasks.
- Review model-generated implementations involving model training, inference systems, MLOps, and LLM applications.
- Identify bugs, edge cases, performance issues, and failure modes in AI-generated code.
- Compare outputs from multiple frontier models and assess their strengths and weaknesses.
- Apply professional engineering judgment to realistic ML engineering scenarios.
Required Skills & Qualifications:
- 2+ years of professional machine learning engineering experience is required.
- Experience building production ML systems, model deployment infrastructure, LLM applications, or AI-powered products is required.
- Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools is required.
- Ability to evaluate model-generated machine learning implementations and technical tradeoffs is required.
- Experience deploying ML systems to production is preferred.
More About the Opportunity:
This role offers a unique opportunity to contribute to the evaluation and improvement of frontier AI coding models. Spots are limited and filling quickly on a first-come, first-serve basis.
Equal Opportunity Employer:
We hire based on skills and expertise. All qualified candidates are welcome regardless of background, experience, or prior employment history. Applications are reviewed solely on demonstrated technical ability and qualifications.
Apply Now!
Responsibilities
- Use frontier AI coding agents to complete and evaluate complex machine learning and AI engineering tasks.
- Review model-generated implementations involving model training, inference systems, MLOps, and LLM applications.
- Identify bugs, edge cases, performance issues, and failure modes in AI-generated code.
- Compare outputs from multiple frontier models and assess their strengths and weaknesses.
- Apply professional engineering judgment to realistic ML engineering scenarios.
Qualifications
- 2+ years of professional machine learning engineering experience is required.
- Experience building production ML systems, model deployment infrastructure, LLM applications, or AI-powered products is required.
- Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools is required.
- Ability to evaluate model-generated machine learning implementations and technical tradeoffs is required.
- Experience deploying ML systems to production is preferred.
Skills mentioned
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