Country Eligibility: India

As a Delivery Leader for our LLM Training business, you will play a pivotal role in leading our efforts to develop high-quality, foundational LLMs. This position requires managing large teams of software engineers and data scientists dedicated to performing various Supervised Fine Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) tasks. Your leadership will ensure the delivery of superior SFT and RLHF datasets, achieving optimal outcomes in terms of quality, throughput, and cost. Main tasks listed: Lead and manage large teams (20+) of Python, JavaScript, Java (and other long tail of languages) developers to execute technical tasks effectively; Collaborate closely with researcher clients, ensuring their satisfaction with the delivered work; Maintain rigorous review processes to ensure the highest quality of datasets; Oversee the transition of projects from initiation to stable states, taking full responsibility for delivery.

We are seeking an engineer responsible for designing, implementing, and maintaining data-validation workflows inside Docker-based build pipelines. This role involves creating and managing Dockerfile labels, metadata standards, and validation scripts that ensure datasets, schemas, and model artifacts meet quality and compliance requirements before deployment. Main tasks listed: Develop and optimize Dockerfiles with built-in data-validation steps; Implement LABEL metadata for dataset versions, schemas, and lineage; Create validation scripts (Python/Bash) for schema checks, data integrity, and quality control; Integrate validation steps into CI/CD pipelines and enforce fail-on-bad-data checks.

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.

We are looking for Finance Experts with strong domain knowledge and analytical skills to collaborate with our researchers in improving the performance of AI models. This is a unique opportunity to apply your finance expertise across areas such as corporate finance, investment banking, accounting, FP&A, private equity, and consulting, while contributing to the advancement of AI systems. No prior AI experience is required.

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 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.

Mobile: You’ll need a smartphone to complete this project. You’ll use it to record yourself while performing the required tasks. Head Strap / Wearable: You’ll also need a head strap to record first-person videos while doing your chores. We will pay you $20 as a signing bonus and reimbursement for your headstrap purchase after you complete 20 hours of work with us! Note: the work involves recording or sharing personal images, video or data. Check the platform’s consent and privacy terms before applying.