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Synthetic Data Generation Jobs in Colorado (NOW HIRING)

Major League Baseball

Test Engineer

Boulder, CO

$105K - $130K/yr

Experience with AI-assisted testing capabilities such as self-healing automation, synthetic data generation, or test code generation * Playwright experience/expertise * Experience introspecting ...

Major League Baseball

Test Engineer

Boulder, CO · On-site

$105K - $130K/yr

Experience with AI-assisted testing capabilities such as self-healing automation, synthetic data generation, or test code generation * Playwright experience/expertise * Experience introspecting ...

Cymertek Corporation

Machine Learning Engineer

Aurora, CO · On-site

$120 - $180/hr

Experience with data augmentation and synthetic data generation * Ability to collaborate in cross‑functional teams (e.g., engineers, product managers) * Knowledge of edge computing and model ...

Cymertek

Machine Learning Engineer

Aurora, CO · On-site

Experience with data augmentation and synthetic data generation * Ability to collaborate in cross-functional teams (e.g., engineers, product managers) * Knowledge of edge computing and model ...

Apogee Engineering, LLC

Test Engineer

Colorado Springs, CO · On-site

$110K - $130K/yr

... synthesis, and maintain bidirectional traceability including test methods and success criteria. * Design, build, and maintain automated test scripts, custom test harnesses, and data-generation tools ...

Apogee Engineering, LLC

Test Engineer

Colorado Springs, CO · On-site

$110K - $130K/yr

... synthesis, and maintain bidirectional traceability including test methods and success criteria. * Design, build, and maintain automated test scripts, custom test harnesses, and data-generation tools ...

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Synthetic Data Generation information

What is synthetic data generation?

Synthetic data generation is the process of creating artificial datasets that mimic real-world data. This technique is used to supplement or replace actual data for purposes such as machine learning, software testing, and research, especially when real data is scarce, sensitive, or costly to obtain. Synthetic data can help improve model accuracy, protect privacy, and enable innovation by providing diverse and unbiased datasets. It is commonly used in fields like healthcare, finance, and autonomous vehicles.

What are the key skills and qualifications needed to thrive in synthetic data generation?

To excel in a Synthetic Data Generation role, you need a solid background in computer science, statistics, and data science, often supported by a relevant degree and experience in machine learning. Familiarity with tools such as Python, TensorFlow, PyTorch, and synthetic data generation platforms, as well as knowledge of privacy-preserving techniques, is typically required. Strong problem-solving abilities, creativity, and effective communication set top performers apart in this field. These skills and qualities are crucial for creating high-quality, realistic synthetic datasets that support robust AI model development while safeguarding sensitive information.

What are the main challenges faced by professionals working in synthetic data generation, and how can they be addressed?

Professionals in synthetic data generation often encounter challenges such as ensuring the generated data accurately represents real-world scenarios while maintaining privacy and data security. Balancing realism with anonymization is crucial, especially when synthetic data is used for AI model training or testing. Collaboration with data scientists, domain experts, and privacy officers is common to validate data utility and compliance with regulations. Staying current with advances in generative models and data validation techniques also helps address these challenges and contributes to career growth in this rapidly evolving field.

What is the difference between Synthetic Data Generation vs Data Analyst?

AspectSynthetic Data GenerationData Analyst
Required CredentialsKnowledge of data science, programming, and data privacyDegree in statistics, data science, or related field
Work EnvironmentData science teams, research labs, tech companiesBusiness environments, analytics teams, consulting firms
Industry UsageAI development, machine learning, data privacyBusiness insights, reporting, decision-making
Search & Comparison IntentUnderstanding data generation techniques, privacy solutionsAnalyzing data, generating reports, insights

While Synthetic Data Generation focuses on creating artificial data for privacy and model training, Data Analysts interpret existing data to provide business insights. Both roles require data-related skills but serve different purposes within the data ecosystem.

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What job categories do people searching Synthetic Data Generation jobs in Colorado look for?

The top searched job categories for Synthetic Data Generation jobs in Colorado are:

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Cities in Colorado with the most Synthetic Data Generation job openings:

Synthetic Data Generation jobs near you

IT - Technology Specialist - IT Service, Support and Operations | Mobile Testing | Mobile Automation

Spruce Infotech

Englewood, CO • On-site

Full-time

Re-posted 27 days ago

Job description

Job Title: Technology Specialist - IT Service, Support and Operations | Mobile Testing | Mobile Automation Testing
Work Location: EnglewoodCO80111
XXX
Contract duration: 6
Target Start Date: 01 Jun 2026
Does this position require Visa independent candidates only? YES
**FULL ONSITE WORK**
Job Details:
Must Have Skills
xperience with performance testing tools such as JMeter, k6, Blazemeter
Handson experience with AIassisted or AIpowered engineering tools used in performance analysis, test automation, observability, or operational intelligence.
Proficiency in one or more scripting programming languages such as Python, Java, JavaScript, Groovy, or Shell.
Nice to have skills
Familiarity with CICD and DevOps tools
Strong analytical, troubleshooting, and communication skills. Experience analyzing performance metrics across application, infrastructure, and database layers. Understanding of system architecture, distributed systems, microservices, APIs, databases, caching, messaging systems cloudnative platforms
Detailed Job Description
Design and execute endtoend performance engineering strategies for web, API, database, cloud, and distributed systems.Build and maintain performance test frameworks for load, stress, spike, endurance, and scalability testing.Use AIenabled tools and methods to improvetest scenario generationworkload modelingsynthetic data generationtest script creation and maintenanceanomaly detectionbottleneck identificationroot cause analysispredictive capacity planningPartner with development, QA, SRE, DevOps,
Minimum years of experience
8-10 years
Certifications Needed :No
Top 3 responsibilities you would expect the Subcon to shoulder and execute
Design and execute endtoend performance engineering strategies for web, API, database, cloud, and distributed systems
Build and maintain performance test frameworks for load, stress, spike, endurance, and scalability testing
Use AIenabled tools and methods to improve test scenario generation, workload modeling, synthetic data generation, test script creation and maintenance, anomaly detection, bottleneck identification, root cause analysis predictive capacity planning
Interview Process (Is face to face required?)
No
Any additional information you would like to share about the project specs/ nature of work

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