"I can work in the heart of Silicon Valley from the comfort of my home"

About Turing:

Turing is one of the world’s fastest-growing AI companies accelerating the advancement and deployment of powerful AI systems.

Turing helps customers in two ways: Working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies.

About the Role:

Annotators are the core builders of SkillsBench. You will design and write AI evaluation tasks — structured challenges given to large language model (LLM) agents running inside automated environments. Each task you create tests whether an AI agent performs significantly better when given domain-specific knowledge 

skills versus without it. Your tasks directly feed into Turing's commercial AI evaluation pipeline, used by clients. 

What You Will Do:

Write clear, unambiguous task instructions that define exactly what an AI agent must produce, where to save it, and what rules to follow Create reference solutions that demonstrate the correct approach and pass all automated checks Write human-readable verifier descriptions listing every check the automated test suite will run Author domain-specific skill files that teach an AI agent the conventions, workflows, and edge cases relevant to the task — without leaking expected answers Ensure the no-skills variant of each task is identical to the with-skills variant except for the absence of skill files Work within the task structure (instruction, environment, solution, tests) and follow Turing's task quality standards 

Required: 

Bachelor's degree or higher in a relevant technical or domain-specific field (Computer Science, Engineering, Finance, Data Science, Linguistics, etc.)  Experience: 1–3 years in a domain where you have hands-on practical expertise (software development, financial analysis, document processing, data science, etc.)  

Must Have:  

Strong written English; ability to write precise, unambiguous instructions 

Genuine hands-on expertise in at least one of the SkillsBench domains (coding, finance, document generation, audio/ML, etc.) 

Ability to think from an AI agent's perspective — what would a model get wrong without guidance? 

Comfort reading and producing structured file outputs (JSON, DOCX, XLSX, Markdown)  

Nice to Have:  

Prior experience with LLM evaluation, prompt engineering, or AI benchmark design 

Familiarity with Python scripting Experience with Docker or containerised environments 

Domains : 

Power Systems & Control 

Cybersecurity 

Network & System Engineering 

Offer Details:

• Commitments Required:  40 hours per week with overlap 4 hours with PST
• Engagement type  : Contractor assignment(no medical/paid leave)
• Duration of contract : 2 months; [expected start date is next week]
• Location : India, Pakistan, Nigeria, Kenya, Egypt, Ghana, Bangladesh, Turkey, Mexico

About Turing:

Turing is one of the world’s fastest-growing AI companies accelerating the advancement and deployment of powerful AI systems.

Turing helps customers in two ways: Working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies.
About the Role
Testers verify that each task's automated test suite works correctly. You run the reference solution inside a Docker container and confirm it scores 1.0 against the verifier. If it doesn't pass, you diagnose the failure, determine whether the issue lies in the solution, the verifier, or the environment, and escalate accordingly. Testers are the last checkpoint before a task is marked ready to ship.
What You Will Do
Execute reference solutions inside Docker containers and capture the output Run the pytest-based test suite against the solution output
Diagnose failures: distinguish between a broken solution, a misconfigured verifier, an environment issue, or a task design flaw
Report failures clearly to the Pod Lead or Annotator with enough detail to reproduce the issue Track test results in the shared tracker and flag tasks that are consistently failing or require environment changes
Required:
Bachelor's degree in Computer Science, Engineering, or a related technical field  
5+ years in software QA, DevOps, backend development, or automated testing   Comfortable working in a Linux command-line environment Hands-on experience with Docker (building images, running containers, reading logs)
Ability to read and understand Python code and basic pytest output Systematic debugging mindset: 

Ability to isolate whether a failure is in the code, the config, or the environment  
Nice to Have:  
Familiarity with CI/CD pipelines and containerised test environments Experience with domain-specific libraries (e.g., pandas, openpyxl, pdfplumber, librosa, astropy) depending on batch assignment Understanding of how LLM evaluation pipelines work 

Offer Details:

• Commitments Required:  40 hours per week with overlap 4 hours with PST
• Engagement type  : Contractor assignment(no medical/paid leave)
• Duration of contract : 2 months; [expected start date is next week]
• Location : India, Pakistan, Nigeria, Kenya, Egypt, Ghana, Bangladesh, Turkey, Mexico

Evaluation Process (approximately 60 mins) :

• 1 round of interviews (Round 1 - 60 min technical )