Machine Learning Engineer
Keystone Recruitment
Job Overview
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Job Description
Machine Learning Engineer
This is an hourly contract role for a Machine Learning Engineer supporting the evaluation of advanced machine learning systems for a leading AI research initiative. The position focuses on transforming real-world ML engineering workflows into structured evaluation benchmarks for frontier AI models.
About the Role
This role is ideal for experienced ML engineers or applied researchers who enjoy deep technical reasoning, experimentation, and system-level thinking. You will directly contribute to evaluating cutting-edge AI systems on practical machine learning engineering tasks.
Key Responsibilities
- Design and write detailed evaluation suites for ML tasks
- Translate applied ML research into structured benchmarks
- Evaluate AI-generated solutions in training and debugging
- Reason about system design choices, tradeoffs, and performance
- Produce clear, technically precise written assessments
Ideal Qualifications
- 3+ years experience in machine learning engineering or applied ML research
- Hands-on experience with model development, experimentation, and evaluation
- Background in ML research in an industry lab or academic setting
- Strong understanding of ML system design and optimization tradeoffs
- Excellent written communication skills and high attention to technical detail
More About the Opportunity
- Fully remote, asynchronous work on your own schedule
- Project-based engagement with potential extensions
- Weekly payments via Stripe or Wise
- Independent contractor classification
- No access to confidential or proprietary data
Key skills/competency
- Machine Learning
- Evaluation
- Benchmarking
- Research
- System Design
- Optimization
- Experimentation
- Model Development
- Debugging
- Technical Writing
How to Get Hired at Keystone Recruitment
- Customize your resume: Highlight relevant ML engineering experience.
- Research Keystone Recruitment: Review company insights on LinkedIn and Glassdoor.
- Tailor your cover letter: Emphasize structured evaluation skills.
- Prepare technical examples: Demonstrate problem-solving in ML projects.
- Practice your pitch: Be ready to discuss benchmarks and experiments.
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