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Data Augmentation Jobs (NOW HIRING)

United IT Solutions

Senior Data Architect

Manhattan, NY · On-site

$74.25 - $99.25/hr

Expertise in Medallion architecture, batch/streaming pipeline design, external data augmentation. * In-depth understanding of metadata management, data governance, lineage, cataloging, residency and ...

United IT

Senior Data Architect with P&C Domain

$68.75 - $92/hr

Expertise in Medallion architecture, batch/streaming pipeline design, external data augmentation. * In-depth understanding of metadata management, data governance, lineage, cataloging, residency and ...

New

PMG

Structured Data Senior Lead

Dallas, TX · On-site

$66.50 - $89/hr

Drive data augmentation and enrichment initiatives, including: * Attribute expansion and optimization * Product clustering and categorization strategies * Integration of margin, inventory, or ...

Logical Intelligence

AI Engineer in ML Data

San Francisco, CA · On-site +1

$134K - $162K/yr

Develop the infrastructure for data augmentation pipelines and synthetic data generation * Collaborate with other teams to understand their pain points and priorities to define milestones of the ...

Apptronik

Software Engineer - Human Motion Data

Austin, TX

$113K - $136K/yr

Leverage game engines (Unreal Engine or Unity) and physics simulators to build simulated environments for procedural motion generation and data augmentation. * Generate high-volume, high-quality ...

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All JobsData Augmentation Jobs

Data Augmentation information

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$44.5K

$129.7K

$177.5K

How much do data augmentation jobs pay per year?

As of Jun 25, 2026, the average yearly pay for data augmentation in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 per year, depending on experience, location, and employer.

What is the difference between Data Augmentation vs Data Analyst?

AspectData AugmentationData Analyst
Required CredentialsTypically no formal degree, but knowledge of data processing toolsBachelor's degree in statistics, data science, or related field
Work EnvironmentPrimarily in data science, machine learning, or AI teamsBusiness, finance, healthcare, or other industry sectors
Employer & Industry UsageTech companies, AI startups, research institutionsCorporations, consulting firms, government agencies
Common Search & Comparison IntentUnderstanding data preparation techniques for AI modelsAnalyzing data to generate insights and reports

Data augmentation focuses on increasing data diversity for machine learning models, often requiring programming skills. Data analysts interpret and analyze data to support business decisions. While both roles work with data, their goals, skills, and environments differ significantly.

Is ML a high paying job?

Machine Learning (ML) roles, including data augmentation specialists, are generally well-paid due to the high demand for AI skills and specialized knowledge in areas like programming, statistics, and data analysis. Salaries vary based on experience, location, and industry, but many ML positions offer competitive compensation compared to other tech roles.

Which 3 jobs will survive AI?

Data augmentation specialists will continue to be in demand as AI models require high-quality training data. Jobs that involve complex problem-solving, creativity, and emotional intelligence, such as data scientists, AI ethicists, and user experience designers, are also likely to persist. These roles often require specialized skills and adaptability that AI cannot easily replicate.

Which 5 jobs will survive AI?

Data augmentation roles involve creating and improving datasets for machine learning, and they are likely to persist as AI systems require high-quality training data. Jobs in AI model training, data labeling, quality assurance, AI ethics, and domain-specific data specialists are expected to remain in demand due to the ongoing need for human oversight and expertise. Skills in data management, programming, and understanding AI workflows will support job security in these areas.

What does data augmentation do?

Data augmentation in a data augmentation job involves increasing the size and diversity of training datasets by applying transformations such as rotation, scaling, or noise addition to existing data. This process helps improve machine learning model performance and robustness, especially in fields like computer vision and natural language processing. Familiarity with data manipulation tools and programming languages like Python is often required.
AIML - Sr Machine Learning Engineer, Data and ML Innovation

AIML - Sr Machine Learning Engineer, Data and ML Innovation

Apple

Cupertino, CA • On-site

Full-time

Posted 25 days ago

Apple rating

8.1

Company rating: 8.1 out of 10

Based on 662 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers

Job description

Do you want to play a part in the next revolution in Foundation Models? Contribute to model hill climbing for Apple Intelligence features that leverage Apple Foundation Models, and work with the people who built the intelligent products that helps millions of people get things done - just by asking or typing!
The vision for the AI/ML FM Data organization is to improve Foundation Models by leveraging data from a variety of sources: crawl, license, vendor and internal crowd-sourcing. As a Sr ML Engineering on the team, you will drive ML innovations, identify key opportunity areas where data can play a crucial role and experiment with various data augmentation strategies to improve model training efficiency and performance..
Description
We are looking for people with a track record in building models and model-driven products to affect user experiences. Join us, and impact hundreds of millions of customers across billions of their interactions with foundation model powered Apple Intelligence features, that are available on iPhone, iPad, HomePod, Mac, Watch, CarPlay, and tv across more than 30 languages.
- Algorithm development: Define signals that are important in prompts, responses and CoT reasoning steps. These usually require a fine-tuned model for specific use cases.
- Model evaluation: Understand the importance of a balanced eval-set. Ability to perform error analysis to figure out how to improve model capabilities.
- Ablation experiments: Test your data augmentation strategies via ablation experiments. Comfortable debugging training errors, and tune hyper-parameters and data mixture to achieve desired outcome.
- Data processing and data filtering: Ability to efficiently process and filter very large amounts of data, often times messy.
Minimum Qualifications
5+ years of hands on ML engineering experiences.
Master or PhDs in Computer Science, Electric Engineering or Mathematics.
Have prior experience as an ML modeler/scientist/researcher. Knowledgeable in classic machine learning algorithms (SVM, Random Forest, Naive Bayes, KNN etc), as well as comfortable with more modern deep learning frameworks (PyTorch, Tensorflow, Jax).
Familiarity with multi-modal data and large models including image and video.
Possess strong software engineering skills and mindset. Have a high bar for engineering code quality and scalability.
Preferred Qualifications
Hands on experiences with different phases in LLM model training, including LoRA, SFT, RLHF, reward modeling.
A good communicator with clear and concise, active listening and empathy skills.
Are self-motivated and curious. Strive to continually learn on the job.
Have demonstrated creative and critical thinking with an innate drive to improve how things work. Have a high tolerance for ambiguity.

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976

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