Expertise: coding_expert

A well-established company that is leveraging the advanced power of technology to help realize the science-fiction fantasy of collaborative and open-ended computer dialogues, is looking for C++ develoeprs. The engineer will be working together on the definition, design, and delivery of new features with cross-functional teams. The company is developing the next generation of dialog agents, which will have a wide range of uses in areas including education, entertainment, and general question-answering. Main tasks listed: Review the code / solutions generated by an AI system, ensuring adherence to quality standards and best practices; Organize the development cycle, effectively manage project priorities, and specify goals and deadlines; Use your expertise in C/ C++ programming to help resolve difficult coding issues that come up during AI validation; Create a collaborative environment within the team that encourages innovation, communication, and continued improvement.

Focus: Hands-on engineering role focused on designing, building, and deploying Generative AI and LLM-based solutions. The role requires deep technical proficiency in Python and modern LLM frameworks with the ability to contribute to roadmap development and cross-functional collaboration. Design and develop GenAI/LLM-based systems using tools such as Langchain and Retrieval-Augmented Generation (RAG) pipelines. Main tasks listed: Design and develop GenAI/LLM-based systems using tools such as Langchain and Retrieval-Augmented Generation (RAG) pipelines; Implement prompt engineering techniques and agent-based frameworks to deliver intelligent, context-aware solutions; Collaborate with the engineering team to shape and drive the technical roadmap for LLM initiatives; Translate business needs into scalable, production-ready AI solutions.

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.

As a Software Engineering evaluator, you will create cutting-edge datasets for training, benchmarking, and advancing large language models, collaborating closely with researchers. This includes curating code examples, providing precise solutions, and making corrections in Python, JavaScript (including ReactJS), C/C++, Java, Rust, and Go; evaluating and refining AI-generated code for efficiency, scalability, and reliability; and working with cross-functional teams to enhance enterprise-level AI-driven coding…

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.

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 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.

Remote contract work for machine learning engineers on an AI training project: create, solve and validate ML engineering tasks such as implementing or modifying models, building reproducible training or inference workflows, optimizing memory or throughput and debugging numerical or system failures. The listing describes the role as very coding-heavy.