... annotation, preprocessing, and evaluation within established data pipelines * Build and maintain services and APIs that connect ML outputs to downstream systems including MES, HMI, and PLC/controls ...
... annotation, preprocessing, and evaluation within established data pipelines * Build and maintain services and APIs that connect ML outputs to downstream systems including MES, HMI, and PLC/controls ...
Data Annotation Services information
How hard is it to get hired by data annotation?
Getting hired for data annotation services typically requires basic computer skills, attention to detail, and the ability to follow instructions. Many positions are entry-level and may not require prior experience, but familiarity with annotation tools and good accuracy can improve chances of employment.
What are the key skills and qualifications needed to thrive in Data Annotation Services, and why are they important?
To excel in Data Annotation Services, strong attention to detail, data literacy, and a foundational understanding of data labeling processes are essential, often requiring a high school diploma or equivalent. Familiarity with annotation platforms, labeling tools, and sometimes basic knowledge of scripting or data management systems is typically expected. Strong work ethic, consistency, and effective communication skills help individuals stand out in collaborative, deadline-driven environments. These capabilities ensure high-quality, accurate labeled data, which is critical for training reliable machine learning models.
Does data annotation actually pay you?
Data annotation services typically pay workers for labeling data used in machine learning models. Payment rates vary depending on the platform, task complexity, and experience, with many jobs offering hourly or per-task compensation. Reliable platforms often require basic skills in data handling and attention to detail.
Is data annotation real or fake?
Data annotation is a legitimate job that involves labeling data such as images, text, or videos to train machine learning models. It requires attention to detail and familiarity with annotation tools, and it is widely used in AI development. The work is real and essential for creating accurate AI systems.
What is the difference between Data Annotation Services vs Data Labeling Specialists?
| Aspect | Data Annotation Services | Data Labeling Specialists |
|---|---|---|
| Credentials | Typically no formal credentials required; focus on training | Often have training in specific tools or industry standards |
| Work Environment | Collaborative, often remote or in-office teams | Similar, working in teams or independently on labeling tasks |
| Industry Usage | Used by AI/ML companies for training datasets | Employed in similar settings, focusing on labeling data for AI models |
| Search & Comparison Intent | Understanding services offered for data preparation | Looking for roles or tasks related to data labeling |
Data Annotation Services encompass the broader process of preparing and annotating data for AI and machine learning projects, often provided by specialized companies. Data Labeling Specialists are individual professionals or team members who perform the actual labeling tasks within these services. While both are closely related, services refer to the overall offering, whereas specialists are the personnel executing the work.
What are some common challenges faced when working in data annotation services, and how can I address them?
In data annotation services, one common challenge is maintaining consistency and accuracy, especially when handling large datasets or ambiguous data points. Clear annotation guidelines and regular communication with team leads help ensure that everyone interprets the data similarly. Additionally, repetitive tasks can lead to fatigue, so it's important to take scheduled breaks and leverage available annotation tools to streamline workflows. Collaborating with peers to discuss edge cases also helps improve overall data quality and fosters a supportive team environment.
What does a data annotation job do?
A data annotation job involves labeling or tagging data such as images, text, or videos to help train machine learning models. Workers use tools to add metadata, which improves the accuracy of AI systems, often working remotely with flexible schedules and requiring attention to detail. Knowledge of annotation tools and data quality standards is beneficial.
What are data annotation services?
Data annotation services involve labeling or tagging data—such as images, text, audio, or video—to make it understandable for machine learning models. These services are essential in training artificial intelligence systems to recognize patterns, objects, or other relevant information in raw data. Companies use data annotation to improve the accuracy and effectiveness of AI applications, such as self-driving cars, chatbots, and image recognition. Professional annotators or specialized platforms often perform these tasks to ensure high-quality, consistent results.
What job categories do people searching Data Annotation Services jobs in Reno, NV look for? The top searched job categories for Data Annotation Services jobs in Reno, NV are:
What cities near Reno, NV are hiring for Data Annotation Services jobs? Cities near Reno, NV with the most Data Annotation Services job openings:
Other
Posted 18 days ago
Job description
Software Engineer, ML/Computer Vision (Battery Sorting)
The Battery Sorting team at Redwood Materials is building a world-class, ML-enabled sorting platform that uses computer vision and machine learning to classify and route thousands of end-of-life batteries per hour across diverse chemistries and form factors. This role sits at the intersection of software engineering and machine learning, with direct ownership of the production systems powering automated battery sorting on the factory floor. The ideal candidate is equally comfortable debugging a production incident as iterating on a model, and will have the opportunity to generate patents in automated battery classification. This is a high-impact, highly visible role with immediate real-world application in advancing the energy transition.
Hours
Full-time | Schedule may vary depending on site operational needs; flexibility required
Responsibilities will include:
- Develop, test, and maintain production software systems powering automated battery sorting, spanning ML inference, image acquisition, sensor integration, and hardware-adjacent control interfaces
- Train and deploy computer vision models for battery chemistry classification, including dataset annotation, preprocessing, and evaluation within established data pipelines
- Build and maintain services and APIs that connect ML outputs to downstream systems including MES, HMI, and PLC/controls interfaces
- Own observability across the production software stack through structured logging, metrics dashboards, alerting, and on-call triage for inference pipelines and supporting services
- Monitor model performance in production to catch regressions or distribution shifts and drive iterative improvements through data analysis and retraining
- Contribute to infrastructure-as-code and CI/CD workflows to validate, version, and deploy application code and ML model artifacts to production environments
- Collaborate cross-functionally with Controls, Hardware, Manufacturing, DevOps, and IT teams to translate operational needs into software and model improvements
Desired Qualifications:
- B.S. in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience
- 2+ years of industry experience working with machine learning models, preferably in computer vision
- Hands-on experience with ML frameworks and libraries such as PyTorch and OpenCV
- Experience contributing to production codebases and pipelines with an emphasis on clean, well-documented, and well-tested code
- Experience designing and tracking ML experiments using tools such as MLflow
- Familiarity with edge deployment or model optimization techniques for inference (e.g., quantization, TensorRT, ONNX Runtime) in latency-sensitive or resource-constrained environments
- Experience with OCR, image classification pipelines, or multi-sensor and multimodal fusion
- Experience working in or alongside industrial, manufacturing, or operations environments where software interacts with physical systems
- Strong cross-functional communication skills and ability to prioritize and execute in a fast-paced, dynamic environment
- A passion for sustainability and making the world a better place!
Working Conditions:
- Factory floor environment; work schedule may vary depending on site operational needs and flexibility is required
- Willingness and ability to travel to Reno, NV as needed
- Additional working conditions to be confirmed with Hiring Manager
About Redwood Materials
Sourced by ZipRecruiter
Industry
Clean energy equipment manufacturing
Company size
501 - 1,000 Employees
Headquarters location
Carson City, NV, US
Year founded
2017