Language: English

About the projects: we are building LLM evaluation and training datasets to train LLM to work on realistic software engineering problems. One of our approaches, in this project, is to build verifiable SWE tasks based on public repository histories in a synthetic approach with human-in-the-loop; while expanding the dataset coverage to different types of tasks in terms of programming language, difficulty level, and etc. Main tasks listed: Analyze and triage GitHub issues across trending open-source libraries; Set up and configure code repositories, including Dockerization and environment setup; Evaluating unit test coverage and quality; Modify and run codebases locally to assess LLM performance in bug-fixing scenarios.

Job Title: Senior Software Engineer – LLM Evaluation & Repository Validation About the projects: we are building LLM evaluation and training datasets to train LLM to work on realistic software engineering problems. One of our approaches, in this project, is to build verifiable SWE tasks based on public repository histories in a synthetic approach with human-in-the-loop; while expanding the dataset coverage to different types of tasks in terms of programming language, difficulty level, and etc. Main tasks listed: Analyze and triage GitHub issues across trending open-source libraries; Set up and configure code repositories, including Dockerization and environment setup; Evaluating unit test coverage and quality; Modify and run codebases locally to assess LLM performance in bug-fixing scenarios.

Job Title: Senior Software Engineer – LLM Evaluation & Repository Validation About the projects: we are building LLM evaluation and training datasets to train LLM to work on realistic software engineering problems. One of our approaches, in this project, is to build verifiable SWE tasks based on public repository histories in a synthetic approach with human-in-the-loop; while expanding the dataset coverage to different types of tasks in terms of programming language, difficulty level, and etc. Main tasks listed: Analyze and triage GitHub issues across trending open-source libraries; Set up and configure code repositories, including Dockerization and environment setup; Evaluating unit test coverage and quality; Modify and run codebases locally to assess LLM performance in bug-fixing scenarios.

Remote contract work for investment banking professionals on a customer AI project: evaluate and annotate financial materials, transaction documents and case studies, draft and review deliverables such as pitchbooks, valuation models and market analyses, simulate real-world deal scenarios, and give detailed written feedback on AI-generated financial content.

Remote part-time work for film, theater and VFX professionals with a leading AI lab: compare pairs of AI-generated videos on lighting, framing, camera movement, texture, continuity and performance, write short precise reasons for each judgment, check videos against their prompts and join calibration rounds with other raters.

Remote contract work evaluating AI models and agents for a customer project: run pre-defined test scenarios, document findings and anomalies in structured reports, repeat evaluations as required by the project protocol and submit evaluation data on AI training platforms, often within strict timeframes.

Remote contract work for hardware engineers on AI training projects: create rigorous, self-contained hardware engineering problems based on realistic scenarios and develop reference solutions with calculations, simulations or scripts (for example Python, MATLAB, Simulink, SPICE or C/C++).

Remote contract work for aerodynamics specialists on an AI training project: create, solve and validate CFD and aerodynamic tasks through command-line workflows, such as preparing domains and meshes, setting boundary conditions, running simulations, computing lift and drag and checking that results are physically valid. Python is used to automate the workflow.

Remote contract work for mechanical engineers on an AI training project: create, solve and validate engineering tasks across structural, thermal, mechanical and multiphysics domains, such as building computational models, meshes, materials and boundary conditions, running simulations and checking results against physical and design constraints. The listing describes the role as very coding-heavy.

Remote contract work for competitive programmers on an AI training project: solve self-contained coding, logic and debugging challenges designed to test advanced AI models, often in unfamiliar codebases or terminal environments, and produce reproducible solutions.