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Data Annotation Law Jobs in Indiana (NOW HIRING)

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Data Annotation Law information

What are the key skills and qualifications needed to thrive as a Data Annotation Specialist, and why are they important?

To thrive as a Data Annotation Specialist, you need keen attention to detail, strong analytical skills, and a basic understanding of data labeling protocols, often supported by a high school diploma or relevant training. Familiarity with data annotation tools like Labelbox, Supervisely, or CVAT, as well as knowledge of data privacy regulations, is typically required. Excellent communication, time management, and consistency are vital soft skills for collaborating with teams and maintaining annotation quality. These skills ensure accurate, reliable data labeling, which is critical for developing effective machine learning models.

What is the difference between Data Annotation Law vs Data Labeler?

AspectData Annotation LawData Labeler
CredentialsLegal knowledge, compliance certificationsBasic training, attention to detail
Work EnvironmentLegal offices, compliance departmentsData annotation platforms, remote or office settings
Industry UsageLegal, AI compliance, data privacyAI training, machine learning datasets
Search & ComparisonLegal roles, compliance in data annotationData labeling, data annotation jobs

Data Annotation Law involves legal expertise ensuring data annotation processes comply with laws and regulations, often requiring legal certifications. Data Labelers focus on annotating data for AI models, typically with basic training. While both roles work within data annotation, Data Annotation Law emphasizes legal compliance, whereas Data Labelers concentrate on data preparation for machine learning.

What is Data Annotation Law?

Data Annotation Law refers to the legal frameworks and regulations that govern the process of labeling, tagging, or categorizing data for use in machine learning and artificial intelligence applications. These laws address issues such as data privacy, intellectual property, consent, and the ethical use of annotated data. Data Annotation Law ensures that organizations handle data responsibly and comply with national and international standards when using human annotators or automated systems. It is crucial for companies to understand these legal requirements to avoid potential legal liabilities and protect the rights of data subjects.

What are some common challenges faced by professionals working in data annotation for the legal industry, and how can they be addressed?

Professionals in data annotation law often encounter challenges such as interpreting complex legal language, ensuring consistency and accuracy in labeling, and maintaining confidentiality with sensitive information. Collaboration with legal experts and ongoing training in legal terminology are essential to address these issues. Additionally, many organizations implement rigorous quality assurance processes and utilize annotation guidelines to help annotators navigate ambiguous cases and improve overall data quality.
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Data Annotation Law jobs near you
Infographic showing various Data Annotation Law job openings in Indiana as of July 2026, with employment types broken down into 2% Locum Tenens, 33% Full Time, 26% Part Time, 2% Contract, 36% Nights, and 1% Summer. Highlights an 56% Physical, 1% Hybrid, and 43% Remote job distribution.

Senior Computer Vision Engineer

Matrix Design Group LLC

Newburgh, IN • On-site

$99K - $136K/yr

Full-time

Re-posted 22 days ago

Job description

Job Summary:
Matrix Design Group LLC is a company that designs, manufactures, and sells innovative technological products to enhance safety. The Senior Computer Vision Engineer will collaborate with software engineers and product managers to develop vision-based artificial intelligence algorithms, ensuring the design of safe and effective computer vision solutions.
Responsibilities:
• Prepare customer-facing presentations for custom model deployments
• Implement CI/CD pipelines for models, data, and source code
• Work with product and project managers to ensure that projects proceed on time and on budget
• Work with other engineers to develop a working understanding of how the AI is developing
• Document process steps to ensure reasonable human oversight
• Work with other engineers to monitor changes in development and implement transfer learning and knowledge distillation between iterative machine learning models
• Mentor junior engineers and interns
• Participate in code reviews and sprint planning
• Understand and apply best practices for object detection modeling
• Understand and apply TensorFlow, PyTorch, and ONNX core concepts
• Understand and apply hardware accelerator compilation and execution
• Understand and apply Jupyter Notebooks/Google Colab concepts
• Understand and apply source control best practices for machine learning, ETL, and data annotation pipelines
Qualifications:
Required:
• All applicants must be able to provide proof of eligibility to work in the United States.
• Employment is contingent upon the successful completion of the I-9 form, as required by federal law.
• Candidates will be required to undergo an employment verification process before beginning work.
• Bachelor’s degree in a related field, such as computer science, software engineering or data science, is recommended.
• 7+ years of experience in the ML space.
• An expert-level understanding of Python with a focus on ML framework such as TensorFlow or PyTorch.
• Proficient with state-of-the-art object detection algorithms such as YOLO, DETR, and DINO.
• Experience with containerization technologies such as Docker, Kubernetes, etc.
• Experience developing software using one or more of the following languages (C/C++, C#, Python).
• Experience with Linux and/or Windows OS.
• Experience with model, container, and package registries.
• Experience with ML tools such as DVC, MLFlow, Lake FS, Label Studio, Azure ML, Azure AI Foundry.
• Experience with SQL databases, vector embeddings, and vector databases such as Milvus, pgvector, Pinecone, Chroma, etc.
• Experience with multimodal models such as CLIP, GPT-4V, Llama.
• Experience with classical CV algorithms such as Canny Edge Detector, SIFT, RANSAC, Optical Flow, SLAM, etc.
• Strong understanding of source control concepts and CI/CD pipelines.
• Must have strong communication, computer, documentation, presentation, and interpersonal skills.
Preferred:
• Master’s degree in Artificial Intelligence a plus.
Company:
Matrix Design Group (Matrix) is the safety and productivity technology leader for industrial applications where people and mobile equipment work in close proximity. Founded in 1996, the company is headquartered in Newburgh, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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