Develop full-stack backend tooling and services for data annotation, validation, and quality ... Collaborate closely with data, research, and engineering teams to accelerate model training and ...
Develop full-stack backend tooling and services for data annotation, validation, and quality ... Collaborate closely with data, research, and engineering teams to accelerate model training and ...
Collaborate with data, research, and engineering teams to support model training and evaluation ... Prior experience with data annotation, data quality, or evaluation systems * Familiarity with AI/ML ...
Collaborate with data, research, and engineering teams to support model training and evaluation ... Prior experience with data annotation, data quality, or evaluation systems * Familiarity with AI/ML ...
Business Analyst
Denver, CO · On-site
Experience with data annotation, data quality evaluation, or content review workflows * Background in case study writing, academic research, or structured business content creation * Familiarity with ...
Business Analyst
Denver, CO · On-site
Experience with data annotation, data quality evaluation, or content review workflows * Background in case study writing, academic research, or structured business content creation * Familiarity with ...
... with data generation and annotation workflows, including curating datasets, scenarios, and tasks that represent realistic usage Portfolio demonstrating previous evaluation frameworks, research ...
... with data generation and annotation workflows, including curating datasets, scenarios, and tasks that represent realistic usage Portfolio demonstrating previous evaluation frameworks, research ...
... with data generation and annotation workflows, including curating datasets, scenarios, and tasks that represent realistic usage Portfolio demonstrating previous evaluation frameworks, research ...
... with data generation and annotation workflows, including curating datasets, scenarios, and tasks that represent realistic usage Portfolio demonstrating previous evaluation frameworks, research ...
... with data generation and annotation workflows, including curating datasets, scenarios, and tasks that represent realistic usage Portfolio demonstrating previous evaluation frameworks, research ...
... with data generation and annotation workflows, including curating datasets, scenarios, and tasks that represent realistic usage Portfolio demonstrating previous evaluation frameworks, research ...
... with data generation and annotation workflows, including curating datasets, scenarios, and tasks that represent realistic usage Portfolio demonstrating previous evaluation frameworks, research ...
... with data generation and annotation workflows, including curating datasets, scenarios, and tasks that represent realistic usage Portfolio demonstrating previous evaluation frameworks, research ...
... with data generation and annotation workflows, including curating datasets, scenarios, and tasks that represent realistic usage Portfolio demonstrating previous evaluation frameworks, research ...
... with data generation and annotation workflows, including curating datasets, scenarios, and tasks that represent realistic usage Portfolio demonstrating previous evaluation frameworks, research ...
Annotation of collection details for collected materials. * Physical and digital inventory management * Data collection and management from clinical and research sources * Tissue analysis and ...
Annotation of collection details for collected materials. * Physical and digital inventory management * Data collection and management from clinical and research sources * Tissue analysis and ...
Annotation of collection details for collected materials. * Physical and digital inventory management * Data collection and management from clinical and research sources * Tissue analysis and ...
Annotation of collection details for collected materials. * Physical and digital inventory management * Data collection and management from clinical and research sources * Tissue analysis and ...
... A), research and development (R&D), cyber operational technology, and training. Our extensive ... Collaborate on data architecture: Partner with client engineering teams to streamline data ...
... A), research and development (R&D), cyber operational technology, and training. Our extensive ... Collaborate on data architecture: Partner with client engineering teams to streamline data ...
A), research and development (R amp;D), cyber operational technology, and training. Our extensive ... Collaborate on data architecture: Partner with client engineering teams to streamline data ...
A), research and development (R amp;D), cyber operational technology, and training. Our extensive ... Collaborate on data architecture: Partner with client engineering teams to streamline data ...
Data Annotation Research information
What qualifications do I need for data annotation?
Data annotation research roles typically require basic computer skills, attention to detail, and familiarity with annotation tools or platforms. A high school diploma or equivalent is usually sufficient, though some positions may prefer experience with data labeling, machine learning concepts, or specific software. Strong communication skills and the ability to work independently are also beneficial.
What are some common challenges faced in Data Annotation Research roles, and how can they be addressed?
Professionals in Data Annotation Research often encounter challenges such as maintaining consistency in labeling, dealing with ambiguous data, and managing large datasets efficiently. These issues can be addressed by following detailed annotation guidelines, participating in regular calibration sessions with the team, and utilizing annotation tools that support quality control checks. Collaboration with data scientists and project managers is essential to clarify ambiguities and ensure that annotated data meets the project's requirements. Staying proactive in communication and continuous learning helps to minimize errors and improve overall data quality.
Does data annotation actually pay?
Data annotation research jobs typically pay hourly or per task rates, with wages ranging from minimum wage to higher rates depending on experience and complexity of the work. Many positions are freelance or remote, requiring basic skills in data labeling tools and attention to detail. Payment is generally reliable, but rates vary by employer and project.
How hard is it to get hired by data annotation?
Getting hired for a data annotation research role typically requires basic computer skills, attention to detail, and sometimes familiarity with annotation tools or platforms. Many positions are entry-level and do not require advanced education, making the hiring process relatively accessible for those with the right skills and reliability.
What is the difference between Data Annotation Research vs Data Labeling Specialist?
| Aspect | Data Annotation Research | Data Labeling Specialist |
|---|---|---|
| Credentials | Typically requires a background in data science, research methods, or related fields | Often requires basic technical skills and experience with labeling tools |
| Work Environment | Research labs, tech companies, or remote research teams | Data centers, tech companies, or remote labeling teams |
| Industry Usage | Used in AI/ML research, developing annotation methodologies | Used in preparing datasets for machine learning models |
| Search & Comparison Intent | Understanding research-focused roles in data annotation | Looking for practical data labeling jobs |
Data Annotation Research involves exploring new annotation techniques and improving data quality for AI models, often requiring research skills. In contrast, Data Labeling Specialists focus on applying existing labeling tools to annotate datasets efficiently. Both roles are essential in AI development but differ in scope and expertise.
Is data annotation real or fake?
Data annotation is a real and essential process in machine learning and AI development, involving labeling data such as images, text, or audio to train algorithms. Data annotation jobs require attention to detail and often use tools like labeling platforms or software, making them a legitimate employment opportunity in the tech industry.
What is data annotation research?
Data annotation research involves studying and developing methods for labeling data, such as images, text, or audio, to be used in training machine learning models. Researchers in this field focus on improving annotation accuracy, efficiency, and scalability, as well as addressing challenges like bias and consistency. This work is critical because high-quality annotated data is essential for building effective AI systems. Data annotation research often includes exploring new tools, techniques, and guidelines for human annotators or automated labeling systems.
What are the key skills and qualifications needed to thrive as a Data Annotation Researcher, and why are they important?
To thrive as a Data Annotation Researcher, you need strong attention to detail, analytical thinking, and familiarity with data labeling concepts, often supported by a degree in computer science, linguistics, or a related field. Experience with annotation platforms, data management tools, and sometimes knowledge of programming languages like Python are typically required. Excellent communication, problem-solving abilities, and the capacity to work independently set standout contributors apart. These skills ensure high-quality, accurate data labeling, which is crucial for developing reliable AI and machine learning models.
What are popular job titles related to Data Annotation Research jobs in Colorado? For Data Annotation Research jobs in Colorado, the most frequently searched job titles are:
What job categories do people searching Data Annotation Research jobs in Colorado look for? The top searched job categories for Data Annotation Research jobs in Colorado are:
What cities in Colorado are hiring for Data Annotation Research jobs? Cities in Colorado with the most Data Annotation Research job openings:
Other
Posted 4 days ago
Job description
Principal Python Engineer - ML Infrastructure (AI Training)
About the Role
What if your Python expertise could directly shape the infrastructure powering some of the world's most advanced AI systems? We're looking for a Principal Python Engineer to build and optimize the data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on - working on real production code with meaningful, measurable impact.
This is a fully remote, flexible contract role for a senior engineer who thrives at the intersection of systems programming, distributed computing, and AI infrastructure.
About the Role
What if your Python expertise could directly shape the infrastructure powering some of the world's most advanced AI systems? We're looking for a Principal Python Engineer to build and optimize the data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on - working on real production code with meaningful, measurable impact.
This is a fully remote, flexible contract role for a senior engineer who thrives at the intersection of systems programming, distributed computing, and AI infrastructure.
- Organization
: Alignerr - Type
: Hourly Contract - Location
: Remote - Commitment
: 20-40 hours/week
- Design, build, and optimize high-performance Python systems supporting large-scale AI data pipelines and model evaluation workflows
- Develop full-stack backend tooling and services for data annotation, validation, and quality control at scale
- Diagnose and resolve bottlenecks across compute-heavy, distributed systems using advanced async patterns and profiling techniques
- Improve reliability, safety, and performance across existing production Python codebases
- Collaborate closely with data, research, and engineering teams to accelerate model training and evaluation cycles
- Drive architectural decisions through synchronous design reviews and clear technical communication
- 5+ years writing production Python for large-scale infrastructure or platform engineering
- Deep expertise in distributed computing, concurrency, and advanced asynchronous programming patterns
- Fluent in Python internals - including GIL limitations, memory profiling, and performance optimization for compute-heavy workloads
- Experienced full-stack developer with a strong systems programming background
- Clear, confident communicator capable of driving technical strategy and architectural decisions
- Native or fluent English speaker
- Available to commit 20-40 hours per week
- Prior experience with data annotation, data quality, or evaluation systems
- Familiarity with AI/ML workflows, model training, or benchmarking pipelines
- Background in distributed systems architecture or developer tooling
- Exposure to working directly with AI research teams or labs
- Work on real, high-impact production systems used by leading AI research labs
- Fully remote and flexible - work when and where it suits you
- Freelance autonomy with the depth and structure of meaningful, long-term technical work
- Collaborate with top engineers and researchers at the frontier of AI development
- Potential for ongoing work and contract extension as new projects launch