Source Job

• Assess technical accuracy and reproducibility of AWS Trainium/NKI tasks.
• Provide actionable feedback on kernel execution bugs and logic errors.
• Evaluate hardware acceleration inefficiencies and compilation issues.

We source experienced technical specialists to audit AI training tasks and evaluation workflows. The project is remote and freelance, with a focus on technical accuracy and efficiency.

• Evaluate Kubernetes tasks for technical accuracy, realism, and solvability.
• Rigorously test and troubleshoot Kubernetes scenarios to identify issues.
• Provide clear, detailed feedback to improve AI training workflows.

Jobgether is an AI-powered job matching platform that connects candidates to opportunities through objective analysis. The company emphasizes efficiency and fairness in the hiring process.

• Assess software engineering tasks for technical accuracy, realism, and reproducibility.
• Provide actionable feedback on codebase integration issues and logic errors.
• Ensure AI training workflows are rigorous and practically applicable.

Project World Wide sources experienced technical specialists for AI training task auditing. This freelance contract opportunity focuses on ensuring technical rigor and accuracy in AI workflows.

• Evaluate Kubernetes tasks for technical accuracy, realism, and reproducibility.
• Provide clear feedback on orchestration issues, configuration bugs, or logic errors.
• Utilize your deep Kubernetes expertise to audit complex technical scenarios.

Greenhouse is a hiring platform that powers recruitment for modern companies. They are an established firm with a distributed team and a culture focused on innovation and flexibility.

• Design physical objects for 3D printing using open-source CAD software while capturing workflow to train AI models.
• Complete engineering design tasks and validation, including writing Python scripts to support design workflows.
• Work remotely on an ad hoc freelance basis with pay up to $120 per hour for selected tasks.

Prolific connects researchers with a global pool of participants to collect high-quality human behavioral data for AI development. With over 35,000 developers and researchers using the platform, it offers flexible, freelance work with competitive pay and no fixed schedule.

• Audit AWS Serverless and Infrastructure as Code tasks for technical accuracy and realism.
• Evaluate deployment scenarios, architecture logic, and testing criteria.
• Provide clear, actionable feedback to improve AI training and evaluation systems.

Jobgether uses AI-powered matching to connect professionals with freelance roles at partner companies. This project offers independent remote work with competitive hourly rates and flexible scheduling.

• Evaluate developer workflow tasks for technical accuracy, realism, solvability, reproducibility, and alignment with reliable testing and evaluation criteria.
• Audit AI-assisted development scenarios to identify technical inconsistencies, logic errors, workflow inefficiencies, or issues affecting task quality.
• Provide clear, detailed, and actionable feedback that enables improvements to AI training and evaluation tasks.

• Design and implement advanced batch scheduling architectures using frameworks like Volcano or YuniKorn to support multi-node gang scheduling.
• Develop and manage cluster-wide admission control and sophisticated job queueing mechanisms utilizing Kueue to manage high-volume AI workload traffic.
• Collaborate with GPU Systems and Storage teams to ensure the scheduling layer is tightly integrated with bare-metal hardware and storage I/O patterns.

Bitdeer provides comprehensive Bitcoin mining solutions and AI computational infrastructure. The company operates globally with data centers in multiple countries and focuses on AI and blockchain technology.

• Deploy and commission GPU cloud infrastructure across regional and core datacenters, covering hardware, networking, storage, and platform software.
• Act as a hands-on technical escalation point, troubleshooting complex issues across physical and software layers and driving incidents to resolution.
• Establish deployment standards, validation procedures, documentation, and operational practices for a rapidly evolving AI infrastructure environment.

The company builds and operates cutting-edge GPU cloud and AI infrastructure at significant scale. It is a fast-growing international scale-up with a diverse and flexible working environment.

• Lead in-depth technical discovery with engineering teams and customer stakeholders to understand AI inference requirements.
• Translate customer objectives into production-ready architectures and define PoC success criteria.
• Identify recurring workload patterns and communicate insights to Product and Engineering for platform evolution.

They are a partner company focused on AI infrastructure and performance-sensitive AI inference workloads. They have an international, engineering-led team solving complex challenges at the forefront of AI.

• Evaluate LLM responses for accuracy, clarity, and completeness.
• Fact-check technical claims using authoritative references.
• Validate code and outputs, and annotate model performance.

Prolific builds the largest pool of high-quality human data for AI development, serving over 35,000 AI developers, researchers, and organizations. They connect researchers with a global community to collect ethically sourced behavioral data.

• Design and implement novel training optimization techniques for large-scale neural networks.
• Investigate approaches to improve training efficiency, stability, and convergence speed.
• Collaborate with infrastructure and inference engineering teams to translate research into production performance.

This partner company focuses on AI research and training optimization for large-scale models. They maintain a small, senior-level team that values deep technical thinking and thoughtful execution.

• Evaluate AWS Serverless and IaC tasks for technical accuracy and reliability.
• Provide clear, actionable feedback on deployment pipeline errors and architecture issues.
• Ensure tasks are realistic, reproducible, and supported by robust tests.

We source experienced technical specialists to audit tasks used to train AI systems. Our project ensures that AI training workflows are technically rigorous and accurate, with a focus on high-quality deliverables.

• Apply deep subject-matter expertise to AI model evaluation and large language model projects.
• Develop challenging domain-specific problems and assess AI responses for accuracy and reasoning.
• Collaborate with AI research teams to improve training datasets and evaluation methodologies.

Jobgether is an AI-powered job matching platform that connects candidates with hiring companies. It offers a remote, asynchronous work culture and uses AI tools to support recruitment.

• Evaluate AI-generated coding interactions end to end for usefulness, accuracy, and consistency with strong engineering practices.
• Assess whether coding agents demonstrate sound technical reasoning and practical engineering judgment rather than just producing working-looking code.
• Provide actionable feedback, distinguishing between adequate and exceptional AI response quality to shape evaluation standards.

Jobgether uses an AI-powered matching process to ensure your application is reviewed quickly and fairly against the role's core requirements. They are a third-party recruitment platform that partners with companies to fill positions, with a streamlined selection process.

• Write detailed outlines of your regular workflows, focusing on one critical task performed at least weekly.
• Provide structured evaluation tasks and nuanced feedback to train AI models.
• Complete paid tasks remotely on a freelance basis, with most tasks requiring one hour of uninterrupted work.

Prolific builds the world's largest pool of quality human data for AI development. With over 35,000 AI developers and organizations using its platform, it focuses on ethically sourced behavioral data from paid participants.

• Build the environments and verifiers our models train against
• Own the synthetic data pipeline, from generation through quality gates
• Ship models into production and keep improving them on real usage

LiveKit builds the infrastructure layer for the voice-driven era of computing, enabling developers to build, deploy, and scale voice AI applications. Founded in 2021, they power voice AI for major companies and have a small, senior team that values craft and creativity.