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Remote Ai Annotation Writing Jobs in Virginia (NOW HIRING)

BlackSky

Staff Engineer, AI

Herndon, VA · On-site +1

Collaborate with the Data QA team to define annotation standards, resolve taxonomy issues, and ... Experience working with remote sensing imagery including geometry, radiometric normalization ...

micro1 AI

M&A Attorney - AI Trainer - Remote

Roanoke, VA · Remote

$90 - $130/hr

Remote We are seeking seasoned M&A attorneys for a part-time role at the forefront of legal AI ... Exceptional written and verbal communication skills with meticulous attention to detail. * Strong ...

micro1 AI

M&A Attorney - AI Trainer - Remote

Richmond, VA · Remote

$90 - $130/hr

Remote We are seeking seasoned M&A attorneys for a part-time role at the forefront of legal AI ... Exceptional written and verbal communication skills with meticulous attention to detail. * Strong ...

micro1 AI

M&A Attorney - AI Trainer - Remote

Suffolk, VA · Remote

$90 - $130/hr

Remote We are seeking seasoned M&A attorneys for a part-time role at the forefront of legal AI ... Exceptional written and verbal communication skills with meticulous attention to detail. * Strong ...

micro1 AI

M&A Attorney - AI Trainer - Remote

Hampton, VA · Remote

$90 - $130/hr

Remote We are seeking seasoned M&A attorneys for a part-time role at the forefront of legal AI ... Exceptional written and verbal communication skills with meticulous attention to detail. * Strong ...

micro1 AI

M&A Attorney - AI Trainer - Remote

Norfolk, VA · Remote

$90 - $130/hr

Remote We are seeking seasoned M&A attorneys for a part-time role at the forefront of legal AI ... Exceptional written and verbal communication skills with meticulous attention to detail. * Strong ...

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All JobsAi Annotation Writing JobsRemote Ai Annotation Writing JobsRemote Ai Annotation Writing Jobs in Virginia

Remote Ai Annotation Writing information

What are some common challenges faced in remote AI annotation writing, and how can they be overcome?

Remote AI annotation writing often requires maintaining high accuracy and consistency while labeling large datasets, which can be repetitive and detail-oriented. Common challenges include understanding ambiguous content, managing distractions in a home environment, and staying updated with changing annotation guidelines. To overcome these, it's helpful to set up a dedicated workspace, communicate regularly with your team or project managers for clarification, and utilize provided training resources or feedback. Maintaining a steady workflow and taking regular breaks also helps reduce errors and burnout.

What is remote AI annotation writing?

Remote AI annotation writing involves labeling, categorizing, or adding descriptive information to data—such as text, images, audio, or video—to help train artificial intelligence and machine learning models. Workers in this role typically use specialized platforms to tag or classify data according to specific guidelines, all while working from home or another remote location. This work is essential for improving the accuracy and effectiveness of AI systems, such as those used in natural language processing or computer vision. Annotations might include identifying objects in images, transcribing audio, or highlighting sentiment in text. The job often requires attention to detail, consistency, and sometimes subject matter expertise depending on the project.

What is the difference between Remote Ai Annotation Writing vs Remote Data Labeling Specialist?

AspectRemote Ai Annotation WritingRemote Data Labeling Specialist
Primary RoleCreating and editing annotations for AI training dataLabeling and categorizing data for machine learning models
Skills RequiredAttention to detail, understanding of annotation tools, basic AI knowledgeData organization, accuracy, familiarity with labeling software
Work EnvironmentRemote, often flexible hoursRemote, often flexible hours
Industry UsageAI development, machine learning projectsAI, autonomous vehicles, healthcare, and more

Both roles involve working remotely to support AI projects, but Remote Ai Annotation Writing focuses on creating detailed annotations for training data, while Remote Data Labeling Specialist emphasizes categorizing and labeling data accurately. Understanding these differences helps job seekers find the right position aligned with their skills and career goals.

What are the key skills and qualifications needed to thrive as a Remote AI Annotation Writer, and why are they important?

To thrive as a Remote AI Annotation Writer, you need strong attention to detail, excellent written communication skills, and the ability to follow complex guidelines, typically supported by a background in linguistics, writing, or a related field. Familiarity with annotation platforms, data labeling tools, and sometimes basic knowledge of programming languages like Python can be beneficial. Adaptability, time management, and the ability to work independently are crucial soft skills for remote collaboration and meeting project deadlines. These skills ensure high-quality, accurate data annotation, which is essential for training reliable AI systems.
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Remote Ai Annotation Writing jobs near you

Remote- AI & Financial Engineering Developer- ONLY W2

INFT Solutions Inc

Mclean, VA • On-site, Remote

Contractor

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

AI & Financial Engineering Developer

Location: McLean, Remote

Call notes:

This is a remote opportunity.
We use a variety of quantitative models to forecast mortgage defaults and prepayments in order to assess financial risk.
The goal is to leverage AI to assist users throughout the model execution lifecycle, including formatting inputs, interpreting data elements, and providing guidance during model execution.
Since we have different models for different mortgage products, the AI should be able to understand the specific model being executed and provide contextual assistance accordingly.
The AI should be capable of analyzing the underlying model code and business logic to explain what is happening during execution, identify potential issues, and help diagnose model outputs.
This role requires a unique combination of AI expertise and Financial Engineering knowledge, as the individual will be working at the intersection of both domains.
Development will primarily be done in Python.
Candidates should have experience with quantitative financial models, including prepayment models, credit risk models, valuation models, and risk models.
Similar to industry-standard models (e.g., Opus), all models go through required security and governance checks before being deployed. They are then hosted securely within internal endpoints for enterprise use.
Job Description: AI & Financial Engineering Developer
Location: McLean, Remote
Must Have Qualifications: 7+ years of software development experience, including experience with API development, AI application development, and programming languages such as Python, C++, and Scala. Candidates should have 1-3 years of financial industry experience, with exposure to large language models (LLMs) and agentic AI development is a strong plus. A degree is preferred but not required. Prior experience with Fannie or Freddie is a strong plus.
Position Overview
We are seeking a highly skilled AI & Financial Engineering Developer who combines deep expertise in artificial intelligence/machine learning with quantitative finance and financial engineering. This hybrid role is ideal for a technologist who thrives at the intersection of cutting-edge AI and complex financial systems.
Key Responsibilities
AI & Machine Learning
• Design, develop, and deploy machine learning models and AI-powered applications for financial use cases
• Build and optimize deep learning, NLP, and generative AI solutions
• Develop data pipelines and feature engineering frameworks for model training and inference
• Implement MLOps best practices including model versioning, monitoring, and continuous deployment
• Stay current with state-of-the-art AI research and evaluate applicability to financial domains
Financial Engineering
• Develop quantitative models for pricing, risk management, and portfolio optimization
• Implement algorithmic trading strategies and backtesting frameworks
• Build financial simulation engines (Monte Carlo, stochastic modeling, etc.)
• Design and develop derivatives pricing models and fixed-income analytics
• Create real-time market data processing and analytics systems
Software Development
• Write production-quality, scalable, and maintainable code
• Architect and build high-performance distributed systems
• Develop RESTful APIs and microservices for financial applications
• Implement robust testing, CI/CD pipelines, and documentation practices
• Collaborate with cross-functional teams including traders, quants, risk managers, and data engineers
Required Qualifications
• Education: Master’s or PhD in Computer Science, Financial Engineering, Quantitative Finance, Mathematics, Physics, or a related quantitative field
• Experience: 7+ years of professional software development experience, with at least 3 years in AI/ML and 2+ years in financial services or fintech
• Programming Languages: Expert proficiency in Python; strong skills in C++, Java, or Scala
• AI/ML Expertise: Hands-on experience with TensorFlow, PyTorch, scikit-learn, and large language models (LLMs)
• Financial Knowledge: Strong understanding of financial instruments (equities, fixed income, derivatives, structured products), market microstructure, and quantitative risk measures (VaR, Greeks, CVA)
• Mathematics: Advanced knowledge of stochastic calculus, linear algebra, probability theory, and numerical methods
• Data & Infrastructure: Experience with SQL/NoSQL databases, cloud platforms (AWS, Azure, or GCP), and big data technologies (Spark, Kafka)
Preferred Qualifications
• CFA, FRM, or equivalent financial certification
• Experience with reinforcement learning applied to trading or portfolio management
• Knowledge of blockchain/DeFi protocols and smart contract development
• Familiarity with regulatory frameworks (Basel III/IV, MiFID II, Dodd-Frank)
• Publications in AI/ML or quantitative finance journals
• Experience with real-time streaming systems and low-latency architectures
• Proficiency with LLM fine-tuning, RAG architectures, and AI agents for financial applications
Technical Stack (Preferred Experience)
Category Technologies
Languages Python, C++, Java, SQL, R
AI/ML PyTorch, TensorFlow, Hugging Face, LangChain, scikit-learn
Finance Libraries QuantLib, Zipline, Backtrader, pandas, NumPy
Cloud & Infra AWS/Azure/GCP, Docker, Kubernetes, Terraform
Data Spark, Kafka, Airflow, PostgreSQL, MongoDB, Redis
DevOps Git, CI/CD, MLflow, Weights & Biases