Machine Learning Engineer
Memphis, TN · On-site
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
Machine Learning Engineer
Memphis, TN · On-site
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
Machine Learning Engineer
Jackson, TN · On-site
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
Machine Learning Engineer
Jackson, TN · On-site
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
Machine Learning Engineer
Nashville, TN · On-site
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
Machine Learning Engineer
Nashville, TN · On-site
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
Machine Learning Engineer
Knoxville, TN · On-site
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
Machine Learning Engineer
Knoxville, TN · On-site
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
Machine Learning Engineer
Chattanooga, TN · On-site
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
Machine Learning Engineer
Chattanooga, TN · On-site
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection,
Data Curation information
See Tennessee salary details
$10.92 - $16.50
9% of jobs
$16.50 - $22.08
10% of jobs
$22.08 - $27.66
4% of jobs
$29.99 is the 25th percentile. Wages below this are outliers.
$27.66 - $33.24
3% of jobs
$33.24 - $38.82
10% of jobs
$38.82 - $44.40
5% of jobs
The median wage is $47.19 / hr.
$44.40 - $49.98
15% of jobs
$49.98 - $55.56
8% of jobs
$59.05 is the 75th percentile. Wages above this are outliers.
$55.56 - $61.14
14% of jobs
$61.14 - $66.72
8% of jobs
$66.72 - $72.30
11% of jobs
$10
$45
$72
How much do data curation jobs pay per hour?
As of Sep 14, 2026, the average hourly pay for data curation in Tennessee is $45.24, according to ZipRecruiter salary data. Most workers in this role earn between $30.09 and $59.44 per hour, depending on experience, location, and employer.
What is a data curation?
A Data Curation job involves collecting, organizing, maintaining, and ensuring the quality of data for accuracy and accessibility. Data curators clean and structure datasets, manage metadata, and ensure compliance with data governance standards. They work closely with data scientists, analysts, and engineers to support data-driven decision-making. This role is essential in industries like research, healthcare, finance, and technology, where high-quality data is crucial for insights and innovation.
What are the key skills and qualifications needed to thrive in data curation, and why are they important?
To excel in Data Curation, you need a strong background in data management, information science, and database technologies, often supported by a degree in a related field. Familiarity with data wrangling tools, metadata standards, programming languages (such as Python or R), and data management systems is highly valuable. Attention to detail, analytical thinking, and collaborative communication are standout soft skills in this position. These abilities ensure data integrity, usability, and accessibility, which are essential for supporting robust decision-making and research outcomes.
What are some typical challenges faced by professionals in data curation roles?
Professionals in Data Curation often manage large, complex datasets from diverse sources, which can present challenges in ensuring consistency, accuracy, and proper documentation. They may encounter incomplete or poorly formatted data that requires significant cleaning and standardization. Collaborating with researchers, data engineers, and subject matter experts is common to clarify requirements and maintain data integrity. Staying current with evolving data standards and technologies is also key to success, making adaptability important in daily work.
How do you become a data curator?
To become a data curator, you typically need a bachelor's degree in a related field such as information science, computer science, or data management. Gaining experience with data organization, metadata standards, and tools like database management systems or data cleaning software is important, along with developing strong analytical and attention-to-detail skills.
Is a data curator a good job?
A data curator manages and organizes data sets to ensure quality, consistency, and accessibility, often using tools like databases and data management software. It is a growing field with opportunities in industries such as healthcare, finance, and technology, and typically requires strong attention to detail and knowledge of data standards. The role can offer stable employment and the chance to develop specialized skills in data management.
What does a data curator do?
A data curator is responsible for organizing, maintaining, and ensuring the quality of data within a database or repository. They select relevant data, apply standards for consistency, and often use tools like data management software to prepare data for analysis or sharing.
What are popular job titles related to Data Curation jobs in Tennessee?
For Data Curation jobs in Tennessee, the most frequently searched job titles are:
What job categories do people searching Data Curation jobs in Tennessee look for?
The top searched job categories for Data Curation jobs in Tennessee are:
Machine Learning Engineer
Memphis, TN • On-site
Bespoke Labs
Full-time
Re-posted 28 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