Create an Account
Create an account for powerful AI tools, award-winning courses, and access to our vibrant community.
or
Already have an account?
Create an account for powerful AI tools, award-winning courses, and access to our vibrant community.
Already have an account?
Join 250,000+ professionals and teams at Microsoft, Shopify, and even NASA. 🚀
Already have an account? Login
Find the best remote jobs. Answer a few questions and we'll deploy a powerful assistant to help you search, create alerts, and more.
1 What roles are you open to?
2 Experience level
3 Work style
Did you know? If memory is enabled, Writing.io can remember your job search preferences and help you to improve your resume, craft customized outreach and more.
Category
Tests AI systems for vulnerabilities and biases by attempting to break or misuse models to improve their safety and robustness.
Evaluates and rates Turkish music production outputs to train AI models on quality assessment and cultural nuance.
Senior backend engineer designs and builds reinforcement learning environments to train AI models on cloud infrastructure, DevOps, and systems design scenarios.
Role Title: Senior Backend Engineer
Role Type: Contractor (20 hrs perweek)
Location: Remote
micro1 is engaging Senior Backend Engineers to participate in an advanced project for a customer, focused on creating sophisticated Reinforcement Learning Environments for AI model training and evaluation. In this role, you’ll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.
As an expert, you will create Reinforcement Learning Environments that test an AI model’s ability to design, deploy, troubleshoot, secure, scale, and recover production-grade cloud infrastructure. You will develop realistic scenarios involving distributed systems, networking, IAM, queues, durable storage, observability, rolling deployments, and disaster recovery, then build reproducible environments, deterministic validation tests, golden reference solutions, and intentionally defective variants.
Scope of Work
Preferred Qualifications
Process:
Compensation Structure
Compensation is output-based; experts are paid per task that meets the project specifications. The time required to complete work may vary depending on the expert’s experience and workflow. Minimum submission requirements apply. Experts must submit a minimum of tasks per week.
Start Timeline & Availability
We typically fill roles within 48 hours and are looking for experts ready to jump in right away. If selected, we expect you to start your first tasks within 24–48 hours of completing onboarding.
micro1 is a US-based technology company focused on AI-powered hiring and talent solutions. It connects companies with highly skilled remote technical professionals and uses AI-driven assessments and interviews to evaluate candidates.
The company works across areas such as software engineering, AI/ML, data, cloud, DevOps, and other technical domains. For specialized projects, micro1 also engages experts to contribute to AI training and evaluation, including creating real-world technical environments and scenarios that help improve AI models.
For this particular opportunity, candidates are being engaged as remote contractors for approximately 20 hours per week, working on advanced AI projects where their backend/cloud expertise is used to create realistic environments for training and evaluating AI systems.
Design and build reinforcement learning environments with realistic cloud infrastructure scenarios to train and evaluate AI models' systems design and troubleshooting capabilities.
Role Title: Senior Backend Engineer
Role Type: Contractor (20 hrs perweek)
Location: Remote
micro1 is engaging Senior Backend Engineers to participate in an advanced project for a customer, focused on creating sophisticated Reinforcement Learning Environments for AI model training and evaluation. In this role, you’ll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.
As an expert, you will create Reinforcement Learning Environments that test an AI model’s ability to design, deploy, troubleshoot, secure, scale, and recover production-grade cloud infrastructure. You will develop realistic scenarios involving distributed systems, networking, IAM, queues, durable storage, observability, rolling deployments, and disaster recovery, then build reproducible environments, deterministic validation tests, golden reference solutions, and intentionally defective variants.
Scope of Work
Preferred Qualifications
Process:
Compensation Structure
Compensation is output-based; experts are paid per task that meets the project specifications. The time required to complete work may vary depending on the expert’s experience and workflow. Minimum submission requirements apply. Experts must submit a minimum of tasks per week.
Start Timeline & Availability
We typically fill roles within 48 hours and are looking for experts ready to jump in right away. If selected, we expect you to start your first tasks within 24–48 hours of completing onboarding.
micro1 is a US-based technology company focused on AI-powered hiring and talent solutions. It connects companies with highly skilled remote technical professionals and uses AI-driven assessments and interviews to evaluate candidates.
The company works across areas such as software engineering, AI/ML, data, cloud, DevOps, and other technical domains. For specialized projects, micro1 also engages experts to contribute to AI training and evaluation, including creating real-world technical environments and scenarios that help improve AI models.
For this particular opportunity, candidates are being engaged as remote contractors for approximately 20 hours per week, working on advanced AI projects where their backend/cloud expertise is used to create realistic environments for training and evaluating AI systems.
Record everyday activities from a first-person perspective using a mobile app to provide training data for an AI project.
Collect and record data in French and English to help train AI models for language development projects.
Ex-MBB consultant creates realistic consulting scenarios and structured tasks to train AI models on high-level business reasoning and problem-solving.
Toloka AI supports frontier model post-training by building domain-specific reinforcement learning environments, tasks, and evaluation frameworks designed by real practitioners.
Mindrift, powered by Toloka — a leading enterprise AI and machine learning data partner since 2014 — connects top domain experts with cutting-edge AI initiatives. Backed by Toloka’s deep expertise in scalable data generation, crowd technology, and applied ML systems, Mindrift enables experts to shape how next-generation generative models learn, reason, and perform.
We are launching a Management Consulting domain focused on translating real-world consulting engagements into structured learning environments for advanced AI systems. To do this credibly, we are assembling a team of strategy consultants from top-tier firms who can convert authentic project experience into end-to-end examples — from problem structuring and work planning to analysis, synthesis, and client-ready recommendations.
You will join a growing team of consultants from leading strategy firms shaping how AI learns high-level business reasoning.
Important: This role is exclusively for consultants with direct experience at a top-tier strategy consulting firm. If you do not have hands-on project experience at one of the firms listed below, please do not apply. This requirement ensures the domain is built by practitioners trained to the highest standards of structured problem-solving and client delivery.
Eligible firms: McKinsey & Company, Boston Consulting Group (BCG), Bain & Company, Oliver Wyman, Roland Berger, Monitor Deloitte (Deloitte S&C), EY-Parthenon, Kearney, and Strategy& (PwC).
Consultants with 3+ years of experience at one of the firms listed above, with hands-on project experience in:
No deep technical background is required — we will onboard you on the lightweight tools involved.
This is a remote, project-based, individual-contributor role focused on analytical design and evaluation.
On this project, contributors can earn up to $60 per hour equivalent, depending on their level and pace of contribution.
Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.
For this project, tasks are estimated to require around 25-30 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.
Ex-MBB consultant creates realistic consulting scenarios and structures learning tasks to train AI models on high-level business reasoning and problem-solving.
Toloka AI supports frontier model post-training by building domain-specific reinforcement learning environments, tasks, and evaluation frameworks designed by real practitioners.
Mindrift, powered by Toloka — a leading enterprise AI and machine learning data partner since 2014 — connects top domain experts with cutting-edge AI initiatives. Backed by Toloka’s deep expertise in scalable data generation, crowd technology, and applied ML systems, Mindrift enables experts to shape how next-generation generative models learn, reason, and perform.
We are launching a Management Consulting domain focused on translating real-world consulting engagements into structured learning environments for advanced AI systems. To do this credibly, we are assembling a team of strategy consultants from top-tier firms who can convert authentic project experience into end-to-end examples — from problem structuring and work planning to analysis, synthesis, and client-ready recommendations.
You will join a growing team of consultants from leading strategy firms shaping how AI learns high-level business reasoning.
Important: This role is exclusively for consultants with direct experience at a top-tier strategy consulting firm. If you do not have hands-on project experience at one of the firms listed below, please do not apply. This requirement ensures the domain is built by practitioners trained to the highest standards of structured problem-solving and client delivery.
Eligible firms: McKinsey & Company, Boston Consulting Group (BCG), Bain & Company, Oliver Wyman, Roland Berger, Monitor Deloitte (Deloitte S&C), EY-Parthenon, Kearney, and Strategy& (PwC).
Consultants with 3+ years of experience at one of the firms listed above, with hands-on project experience in:
No deep technical background is required — we will onboard you on the lightweight tools involved.
This is a remote, project-based, individual-contributor role focused on analytical design and evaluation.
On this project, contributors can earn up to $60 per hour equivalent, depending on their level and pace of contribution.
Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.
For this project, tasks are estimated to require around 25-30 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.
Former strategy consultant creates realistic consulting project scenarios and designs structured learning tasks to train AI models on business reasoning and problem-solving.
Toloka AI supports frontier model post-training by building domain-specific reinforcement learning environments, tasks, and evaluation frameworks designed by real practitioners.
Mindrift, powered by Toloka — a leading enterprise AI and machine learning data partner since 2014 — connects top domain experts with cutting-edge AI initiatives. Backed by Toloka’s deep expertise in scalable data generation, crowd technology, and applied ML systems, Mindrift enables experts to shape how next-generation generative models learn, reason, and perform.
We are launching a Management Consulting domain focused on translating real-world consulting engagements into structured learning environments for advanced AI systems. To do this credibly, we are assembling a team of strategy consultants from top-tier firms who can convert authentic project experience into end-to-end examples — from problem structuring and work planning to analysis, synthesis, and client-ready recommendations.
You will join a growing team of consultants from leading strategy firms shaping how AI learns high-level business reasoning.
Important: This role is exclusively for consultants with direct experience at a top-tier strategy consulting firm. If you do not have hands-on project experience at one of the firms listed below, please do not apply. This requirement ensures the domain is built by practitioners trained to the highest standards of structured problem-solving and client delivery.
Eligible firms: McKinsey & Company, Boston Consulting Group (BCG), Bain & Company, Oliver Wyman, Roland Berger, Monitor Deloitte (Deloitte S&C), EY-Parthenon, Kearney, and Strategy& (PwC).
Consultants with 3+ years of experience at one of the firms listed above, with hands-on project experience in:
No deep technical background is required — we will onboard you on the lightweight tools involved.
This is a remote, project-based, individual-contributor role focused on analytical design and evaluation.
On this project, contributors can earn up to $60 per hour equivalent, depending on their level and pace of contribution.
Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.
For this project, tasks are estimated to require around 25-30 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.
Trains AI systems by providing Chinese voice coaching and linguistic expertise to improve model outputs.
Creates and edits video content to train AI systems and improve model outputs.
Coaches AI systems in French language by applying linguistic expertise to improve model performance and accuracy.
Captures gameplay data and interaction patterns to help train and improve AI systems through hands-on gameplay expertise.
CAD engineer creates assignments, reviews AI-generated work, and provides feedback to train and evaluate AI models using domain expertise.
Headquarters:
URL: https://www.toptal.com/
We're looking for CAD Engineers to support the training and evaluation of AI models through task-based work. This isn't traditional CAD production work — you'll apply deep CAD domain expertise to create realistic assignments, define clear requirements and expected outputs, and rigorously review completed work for accuracy. If you can think like both an engineer and an evaluator, this role is built for that.
Analyze CAD domains to identify realistic, representative task scenarios
Create well-defined assignments with clear requirements and expected outputs
Review completed work for technical accuracy and adherence to CAD best practices
Source and validate CAD files to ensure quality and relevance for AI training use cases
Provide clear, structured feedback to support AI model evaluation
Complete a short paid test task, which determines eligibility for larger batches of ongoing work
Strong hands-on CAD engineering experience across relevant domains and tools
Ability to design realistic, well-scoped technical assignments from scratch
Strong attention to detail for reviewing and validating technical work
Comfort working in a task-based, output-driven engagement structure
Ability to source and assess CAD files for quality and appropriateness
Prior experience contributing to AI training, evaluation, or data annotation projects
Familiarity with multiple CAD platforms or specialized CAD domains
Flexible, task-based structure: Work in discrete assignments rather than a fixed long-term commitment
High-leverage impact: Your CAD expertise directly shapes how AI models learn and are evaluated
Low-commitment entry point: Start with a short paid test task before scaling into larger batches of work
Domain expertise valued: Real engineering judgment matters here, not just task completion
Â
To apply: https://weworkremotely.com/remote-jobs/toptal-cad-engineer-ai-model-training-evaluation-remote
Evaluates and tests AI agents/models to identify issues and provide feedback for improvement.
Former MBB consultant designs and builds reinforcement learning tasks, environments, and evaluation frameworks for frontier AI model post-training.
Tests AI product functionality and provides feedback on AI system outputs using a personal device.
Labels and annotates bioacoustic audio data to train AI models for animal communication research, ensuring dataset quality and optimizing annotation workflows.
Annotator
Location: Remote (must be based in the United States)
Team: Research
Employment Type: 1 year, fixed-term contract
Compensation: 25 USD / hour, up to 30 hours a week
Earth Species Project (ESP) is a non-profit using frontier AI to decode animal communication. We believe the exponential progress we’re seeing in AI offers new ways of looking at the world and expanding the ability of human beings to learn from other species. Our hope is that this will make a significant contribution to altering human perspective on how we relate to the rest of nature.
ESP partners with biologists and machine learning researchers at leading universities and institutions around the world, and we are honored to be supported by many forward-looking philanthropists and groups, including Reid Hoffman, Waverley Street Foundation, Allen Family Philanthropies, McGovern Foundation and the National Geographic Society. ESP’s work is scaling rapidly and we are seeking a detail-oriented, mission-driven accounting professional to join our team and help ensure the financial infrastructure that supports our research and growth is accurate, compliant, and built to scale.
We are seeking two Bioacoustics Annotators to serve as an internal data consultant across multiple exciting projects. In this role, you will be the driving force behind creating datasets for training and evaluating AI models focused on processing animal communication data. Annotators will report to senior research scientists, who will oversee annotation efforts and provide additional training in applying analysis tools and interpretation of results.
Audio Annotation: Precision labeling of bioacoustic audio across various diverse projects, for example, vocalization onsets and offsets, species identification, and call type classification. While audio will be your main focus, annotation tasks may include other modalities of data (e.g., voice notes, video).
Pipeline Optimization: Test and provide constructive feedback on our annotation software and user interfaces to help us streamline and speed up the data pipeline.
Annotation Validation: Develop and use software to ensure annotation quality.
Dataset integration and curation: Integrate datasets into pre-existing data infrastructure.
Collaboration on Scientific Projects: Contribute to scientific publications by describing annotation methods in writing. Work with researchers to integrate your observations of data into scientific processes and help interpret results. Attend regular team meetings and discussions.
Education r equirements: Undergrad degree in relevant field (e.g., computer science, marine biology, zoology, acoustics, environmental science) or equivalent experience. Experience with tools like Raven is advantageous for this position.
Bioacoustics experience: You have a solid background handling and interpreting acoustic data and familiarity with data collection protocols and common formats. We are not looking for a specific taxonomic specialty—your expertise could be in birds, marine mammals, insects, or something else.
Coding experience: You are comfortable creating code (Python) to review annotations, summarize datasets with figures, and integrate datasets into ESP’s data platform. You are comfortable navigating software interfaces and interested in helping improve tool usability.
Laser focus & patience: You possess a high level of attention to detail and enjoy deep-focus work.
Strong communication skills: You are able to describe annotation methods verbally and in writing, and provide clear feedback on tools and annotation methodology.
Enthusiasm: Data annotation can be repetitive, and it requires a high degree of concentration. This role is well-suited for someone who enjoys repetitive work and listening to different animal sounds
ESP is committed to equal employment opportunities regardless of race, color, religion, gender, gender identity or expression, pregnancy, sexual orientation, marital status, ancestry, national origin, genetics, disability, age, veteran status, and criminal history, consistent with legal requirements. We encourage folks of all backgrounds and perspectives to apply.
There will be 3 stages to the interview - an initial screening, a first interview with our researchers and a final stage, technical assessment. If interested, please apply by Aug 15, 2026. Applications will be reviewed on a rolling basis.
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.
Label and annotate bioacoustic audio data to create training datasets for AI models that decode animal communication.
Annotator
Location: Remote (must be based in the United States)
Team: Research
Employment Type: 1 year, fixed-term contract
Compensation: 25 USD / hour, up to 30 hours a week
Earth Species Project (ESP) is a non-profit using frontier AI to decode animal communication. We believe the exponential progress we’re seeing in AI offers new ways of looking at the world and expanding the ability of human beings to learn from other species. Our hope is that this will make a significant contribution to altering human perspective on how we relate to the rest of nature.
ESP partners with biologists and machine learning researchers at leading universities and institutions around the world, and we are honored to be supported by many forward-looking philanthropists and groups, including Reid Hoffman, Waverley Street Foundation, Allen Family Philanthropies, McGovern Foundation and the National Geographic Society. ESP’s work is scaling rapidly and we are seeking a detail-oriented, mission-driven accounting professional to join our team and help ensure the financial infrastructure that supports our research and growth is accurate, compliant, and built to scale.
We are seeking two Bioacoustics Annotators to serve as an internal data consultant across multiple exciting projects. In this role, you will be the driving force behind creating datasets for training and evaluating AI models focused on processing animal communication data. Annotators will report to senior research scientists, who will oversee annotation efforts and provide additional training in applying analysis tools and interpretation of results.
Audio Annotation: Precision labeling of bioacoustic audio across various diverse projects, for example, vocalization onsets and offsets, species identification, and call type classification. While audio will be your main focus, annotation tasks may include other modalities of data (e.g., voice notes, video).
Pipeline Optimization: Test and provide constructive feedback on our annotation software and user interfaces to help us streamline and speed up the data pipeline.
Annotation Validation: Develop and use software to ensure annotation quality.
Dataset integration and curation: Integrate datasets into pre-existing data infrastructure.
Collaboration on Scientific Projects: Contribute to scientific publications by describing annotation methods in writing. Work with researchers to integrate your observations of data into scientific processes and help interpret results. Attend regular team meetings and discussions.
Education r equirements: Undergrad degree in relevant field (e.g., computer science, marine biology, zoology, acoustics, environmental science) or equivalent experience. Experience with tools like Raven is advantageous for this position.
Bioacoustics experience: You have a solid background handling and interpreting acoustic data and familiarity with data collection protocols and common formats. We are not looking for a specific taxonomic specialty—your expertise could be in birds, marine mammals, insects, or something else.
Coding experience: You are comfortable creating code (Python) to review annotations, summarize datasets with figures, and integrate datasets into ESP’s data platform. You are comfortable navigating software interfaces and interested in helping improve tool usability.
Laser focus & patience: You possess a high level of attention to detail and enjoy deep-focus work.
Strong communication skills: You are able to describe annotation methods verbally and in writing, and provide clear feedback on tools and annotation methodology.
Enthusiasm: Data annotation can be repetitive, and it requires a high degree of concentration. This role is well-suited for someone who enjoys repetitive work and listening to different animal sounds
ESP is committed to equal employment opportunities regardless of race, color, religion, gender, gender identity or expression, pregnancy, sexual orientation, marital status, ancestry, national origin, genetics, disability, age, veteran status, and criminal history, consistent with legal requirements. We encourage folks of all backgrounds and perspectives to apply.
There will be 3 stages to the interview - an initial screening, a first interview with our researchers and a final stage, technical assessment. If interested, please apply by Aug 15, 2026. Applications will be reviewed on a rolling basis.
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.
Reviews and labels transcription/subtitling outputs to improve AI voice model quality through feedback and annotation.
