Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Grand Rapids, MI · On-site
Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Grand Rapids, MI · On-site
Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Berrien Springs, MI · On-site
Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Berrien Springs, MI · On-site
Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Mount Pleasant, MI · On-site
Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Mount Pleasant, MI · On-site
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Lansing, MI · On-site
Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Lansing, MI · On-site
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Warren, MI · On-site
Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Warren, MI · On-site
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Allendale, MI · On-site
Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Allendale, MI · On-site
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Kalamazoo, MI · On-site
Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Kalamazoo, MI · On-site
Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Ann Arbor, MI · On-site
Implement methods from recent ML papers quickly and turn them into production-grade systems
Machine Learning Engineer
Ann Arbor, MI · On-site
Implement methods from recent ML papers quickly and turn them into production-grade systems
Material Sorter
Turner, MI · On-site
$11.75 - $14.75/hr
... grade and type of material and any undesirable elements within the material. * Operate manual processing equipment. Sort/segregate recoverable materials (i.e. metal, paper and plastic) from waste ...
Material Sorter
Turner, MI · On-site
$11.75 - $14.75/hr
... grade and type of material and any undesirable elements within the material. * Operate manual processing equipment. Sort/segregate recoverable materials (i.e. metal, paper and plastic) from waste ...
Paper Grader information
See Michigan salary details
$8.38 - $10.04
23% of jobs
$10.29 is the 25th percentile. Wages below this are outliers.
$10.04 - $11.69
14% of jobs
The median wage is $12.80 / hr.
$11.69 - $13.35
20% of jobs
$13.35 - $15.01
14% of jobs
$15.75 is the 75th percentile. Wages above this are outliers.
$15.01 - $16.67
10% of jobs
$16.67 - $18.32
3% of jobs
$18.32 - $19.98
4% of jobs
$19.98 - $21.64
3% of jobs
$21.64 - $23.29
3% of jobs
$23.29 - $24.95
3% of jobs
$24.95 - $26.61
2% of jobs
$8
$14
$26
How much do paper grader jobs pay per hour?
What is a paper grader?
A Paper Grader reviews and evaluates academic assignments, exams, or essays based on specific grading criteria. They assess students' work for accuracy, completeness, and adherence to guidelines, often providing feedback. Paper Graders are commonly employed by educational institutions or professors to help with large volumes of student submissions. Strong attention to detail and knowledge of the subject matter are essential for this role.
What are the typical daily responsibilities of a paper grader?
Paper Graders are primarily responsible for evaluating written assignments, applying standardized rubrics, and providing clear, constructive feedback to students. On a daily basis, you may sort and prioritize assignments, collaborate with instructors to clarify expectations, and use digital grading platforms to record scores and comments. The workload can vary throughout the academic term, often increasing around midterms and finals, so good organizational skills are essential. Working as a Paper Grader is usually a collaborative process, involving regular communication with teaching staff to ensure consistency and fairness in grading.
What are the key skills and qualifications needed to thrive in the paper grader position, and why are they important?
To thrive as a Paper Grader, you should possess strong attention to detail, proficiency in written communication, and a solid understanding of relevant subject matter, often supported by a college degree or academic coursework. Familiarity with grading management platforms, learning management systems (LMS), and rubrics is typically required. Excellent time management, impartiality, and constructive feedback skills set top candidates apart. These skills ensure accurate, fair, and timely grading that supports both institutional standards and student learning.
Can you get paid to grade papers?
What are the most commonly searched types of Paper Grader jobs in Michigan?
The most popular types of Paper Grader jobs in Michigan are:
What are popular job titles related to Paper Grader jobs in Michigan?
For Paper Grader jobs in Michigan, the most frequently searched job titles are:
- Coal Mining Equipment Operator
- Seasonal Excavator Loader Operator
- Remote Excavator Operator
- No Experience Heavy Equipment Operator Apprentice
- Heavy Equipment Operator Mining
- Remote Pipeline Operator
- Heavy Equipment Loader Operator
- Temporary Heavy Equipment Operator
- Experienced Heavy Equipment Operator
- Overnight Excavator Operator
What job categories do people searching Paper Grader jobs in Michigan look for?
The top searched job categories for Paper Grader jobs in Michigan are:

Full-time
Re-posted 19 days ago
Job description
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Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch
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Build and maintain the infrastructure around RL training: rollout collection, data curation, reward model serving, and experiment orchestration
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Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues
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Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy
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Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams
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Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics
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Implement methods from recent ML papers quickly and turn them into production-grade systems