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Night Shift Remote Data Annotation Jobs in Missouri

$11.50 - $15.50/hr

Explain how data collection, annotation, labeling decisions, model training, evaluation, and ... Benefits: * Remote work: Work remotely from Romania or another location compatible with the ...

This is a fully remote position with a permanent night-shift schedule and a long-term opportunity ... Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process ...

Position Details: 🌙 Monitor Tech - Night Shift Location: Mercy Hospital St. Louis Schedule: 6:30 ... Review patient data to identify patterns and variances * Work closely with nurses and care teams to ...

This role blends remote sensing science with applied data engineering and AI. What You'll Do ... Support annotation workflows by identifying and flagging imagery containing objects of interest

Finance Employment Type: Full-time Shift: Monday - Friday, 8:00 A.M. - 5:00 P.M. Job Summary The ... Lead initiatives to streamline financial processes, enhance use of technology, and strengthen data ...

This is a fully remote project administration role focused on keeping project cost information ... Maintain strong data integrity across project records and proactively identify issues before they ...

Technical Scientist - SME

Springfield, MO · On-site +1

$150K - $235K/yr

... remote sensing analytics. The work is fast-paced and mission-driven -- requirements shift ... Analyze optical signature data to extract target phenomenology, characterize signatures, and ...

Purpose of the Portfolio Revenue Manager position In this remote Portfolio Revenue Manager role ... data for data-driven decision-making. Utilizes revenue management systems, property management ...

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Night Shift Remote Data Annotation information

What is a night shift remote data annotation?

A night shift remote data annotation job involves labeling or tagging data, such as images, text, or audio, to help train machine learning models. The work is performed from home or another remote location during nighttime hours, which can be ideal for those needing flexible schedules or working in different time zones. Data annotators use specific guidelines and tools to ensure accuracy and consistency. This job typically requires attention to detail, basic computer skills, and the ability to follow instructions closely.

What are the key skills and qualifications needed to thrive as a night shift remote data annotation specialist?

To thrive as a Night Shift Remote Data Annotation Specialist, you need strong attention to detail, accuracy, and the ability to work independently, often supported by a high school diploma or equivalent. Familiarity with data labeling tools, annotation software, and basic computer systems is typically required, and experience with platforms like Labelbox or CVAT is beneficial. Excellent time management, self-motivation, and reliable communication skills help you excel in a remote and asynchronous work environment. These skills are crucial to ensure high-quality, consistent data output that directly impacts the effectiveness of machine learning and AI projects.

What are some common challenges faced by night shift remote data annotation professionals, and how can they be managed?

Night shift remote data annotation professionals often encounter challenges such as maintaining focus during late hours, managing fatigue, and staying motivated while working independently. To address these issues, it's helpful to establish a consistent sleep schedule, create a well-lit and organized workspace, and take regular breaks to reduce eye strain and maintain productivity. Additionally, staying connected with team members through scheduled check-ins or chat platforms can help reduce feelings of isolation and foster collaboration, ensuring high-quality annotation work.

What is the difference between Night Shift Remote Data Annotation vs Night Shift Remote Data Labeling?

AspectNight Shift Remote Data AnnotationNight Shift Remote Data Labeling
Primary TaskAdding detailed annotations to datasets, such as bounding boxes or polygonsAssigning labels or categories to data points, often simpler tags
Required SkillsAttention to detail, basic understanding of annotation toolsUnderstanding of categories, quick decision-making
Work EnvironmentRemote, night shift, often part-time or freelanceRemote, night shift, similar flexible hours
Industry UsageUsed in computer vision, autonomous vehicles, AI trainingUsed in machine learning, data categorization tasks

Both roles are remote, night shift positions in AI data processing. Data annotation involves detailed marking of data, while data labeling focuses on categorizing data points. The choice depends on the complexity of tasks and required skills, but both are essential in AI training pipelines.

What is the average salary for night shift remote data annotation jobs?

The average salary for night shift remote data annotation jobs typically ranges from $12 to $20 per hour, depending on experience, company, and location. These roles often require attention to detail and familiarity with annotation tools, with some positions offering additional incentives for night shifts.

What are the most commonly searched types of Shift Remote Data Annotation jobs in Missouri?

The most popular types of Shift Remote Data Annotation jobs in Missouri are:

What are popular job titles related to Night Shift Remote Data Annotation jobs in Missouri?

For Night Shift Remote Data Annotation jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Night Shift Remote Data Annotation jobs in Missouri look for?

The top searched job categories for Night Shift Remote Data Annotation jobs in Missouri are:

What cities in Missouri are hiring for Night Shift Remote Data Annotation jobs?

Cities in Missouri with the most Night Shift Remote Data Annotation job openings:

Lead Instructor: Machine Learning Data Associate

Jobgether

Remote

$11.50 - $15.50/hr

Full-time

Posted 10 days ago


Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead Instructor: Machine Learning Data Associate based in Netherlands.

This is an opportunity to lead engaging, high-impact virtual learning experiences for diverse audiences developing skills in machine learning and AI data workflows. You will deliver live technical instruction in German while collaborating professionally in English with program and operations teams. The role combines technical expertise, instructional leadership, and the ability to make complex AI concepts practical and accessible. You will help learners understand how data labeling influences machine learning systems, generative AI, multimodal models, and applied AI workflows. Sessions may range from smaller groups to audiences of thousands of learners in a highly interactive online environment. You will also contribute to curriculum refinement and continuous course improvement. This is a part-time contract position with a remote setup and scheduled sessions aligned with the EST/CEST time zones.

Accountabilities
  • Lead and facilitate live virtual classes for diverse learner groups, potentially ranging from 100 to more than 8,000 participants, while maintaining strong engagement and an effective learning environment.
  • Deliver technical instruction in German on machine learning data workflows, generative AI, foundation models, prompt engineering, RAG, multimodal AI, responsible AI, and related concepts.
  • Explain how data collection, annotation, labeling decisions, model training, evaluation, and deployment are connected, helping learners understand the downstream impact of their work.
  • Manage virtual classroom dynamics, including Q&A, chat activity, pacing, learner engagement, and timely delivery of planned content.
  • Adapt instructional approaches, explanations, and lesson pacing to accommodate learners with different backgrounds and levels of technical understanding.
  • Prepare thoroughly for each lecture and ensure that technical content is delivered accurately, clearly, and in an accessible manner.
  • Collaborate with program and operations teams in English to support smooth delivery, communicate updates, escalate issues, and maintain alignment on schedules and learner needs.
  • Contribute to curriculum design and continuous improvement, including suggesting edits, refining lessons, and supporting the development of hands-on learning activities.
  • Provide constructive feedback before and after sessions to strengthen course quality and learner outcomes.
  • Maintain a professional, respectful, responsive, and empathetic presence when interacting with learners, colleagues, contractors, and guest speakers.
Requirements:
  • Fluency in German sufficient to deliver all instruction and learner support professionally, combined with strong English proficiency for written communication, meetings, alignment, and issue escalation.
  • Strong practical understanding of the machine learning lifecycle, including data collection, model training, evaluation, and deployment, with the ability to explain how labeling decisions affect model behavior and downstream outputs.
  • Solid knowledge of generative AI and foundation models, including concepts such as pre-training, fine-tuning, reinforcement learning from human feedback (RLHF), and human feedback for model alignment.
  • Hands-on experience with prompt engineering, including zero-shot, one-shot, and few-shot prompting, and the ability to teach learners how to interpret requirements, identify task constraints, and evaluate output quality.
  • Working knowledge of Retrieval-Augmented Generation (RAG) and production AI workflows, including the ability to explain how to assess outputs for relevance, faithfulness, and groundedness.
  • Understanding of multimodal and cross-modal AI, including how models process and generate text, images, audio, and video, and how these capabilities influence annotation and evaluation tasks.
  • Knowledge of responsible AI, privacy, confidentiality, security, bias awareness, and transparency principles relevant to data labeling and AI workflows.
  • Demonstrated experience delivering high-quality live virtual instruction, ideally to large and diverse audiences.
  • Experience supporting technical curriculum development, lesson refinement, skills labs, or similar instructional content.
  • Strong communication, presentation, empathy, adaptability, and classroom-management skills, with the ability to make complex technical concepts easy to understand.
  • A positive and collaborative mindset, with strong attention to preparation, professionalism, responsiveness, and continuous improvement.
  • Preferred: AWS Certified AI Practitioner (AIF-C01) certification or equivalent expertise, particularly experience aligning training content with certification domains and supporting exam readiness.
Benefits:
  • Remote work: Work remotely from Romania or another location compatible with the required time zone.
  • Part-time contract: Flexible engagement structured around scheduled instructional sessions and program needs.
  • Virtual-first environment: Deliver impactful learning experiences entirely online using modern virtual classroom tools.
  • High-impact teaching: Reach diverse learner populations and contribute to workforce development in AI and data.
  • Professional collaboration: Work alongside program, operations, and instructional teams in an international environment.
  • Curriculum involvement: Contribute ideas and expertise to the development and continuous improvement of technical training content.
  • Potential schedule flexibility: Program dates and lecture times may be adjusted depending on program requirements and instructor availability.
  • Inclusive environment: Participate in a learning culture focused on respect, accessibility, diversity, and equal opportunity.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
 Why Apply Through Jobgether? 
 
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
 
 
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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