Jobs at EVB

Review and improve Bluebook citation and pincite grading rubrics, identify ambiguous rules and edge cases, and create sample cite-checking tasks with realistic citation errors, corrections, and explanations. Apply citation rules consistently, flag unresolved ambiguities, and deliver detailed editorial work independently and on schedule.

Leads the Physical AI research agenda across robotics, embodied agents, robot learning, evaluation, simulation, and physical-world R&D automation. The role builds agents connecting foundation models to robots and instruments, develops scalable data and evaluation systems, advances sim-to-real transfer, establishes research infrastructure, recruits and leads a world-class team, and partners with leading AI and robotics laboratories.

Own the full data partnership lifecycle, from sourcing and engaging companies to discovery, privacy and security discussions, commercial negotiations, closing, and onboarding. Build executive relationships, explain AI data applications, manage partnership pipelines, collaborate with internal teams, and develop scalable acquisition strategies and playbooks.

Create challenging data science and machine learning tasks with reproducible environments, verified reference solutions, and robust evaluation tests. Analyze coding-agent failures, identify implementation errors, data leakage, and flawed assumptions, then refine tasks and document requirements, reasoning, and verification steps. The role requires practical programming, mathematical reasoning, and experience with competitive data science platforms such as Kaggle or DrivenData.

Build and improve AI recruiting agents using modern LLMs, Python backend services, APIs, prompts, RAG, workflows, and agent orchestration. Evaluate model performance, experiment with AI technologies, collaborate with product and engineering teams, and deploy scalable production systems. The role requires Python, LLM and AI API experience, familiarity with agent frameworks and FastAPI, and cloud platform knowledge. Production AI product experience, model evaluation, tool calling, prompt engineering, and open-source contributions are preferred.

Build realistic coding tasks involving feature implementation and bug fixes for AI systems. Develop deterministic verifiers that accept valid solutions and reject invalid ones. Apply backend expertise in scalable services and APIs, debugging, performance optimization, automated testing, code review, refactoring, and complex feature development. The role is remote, contractor-based, flexible, and output-compensated, with work expected to begin shortly after onboarding.

Build and deploy AI systems for leading labs and enterprise partners. Responsibilities include designing data intelligence platforms, ML and evaluation pipelines, taxonomies, labeling workflows, LLM applications, multi-agent and RAG systems, inference infrastructure, and human-in-the-loop workflows. The engineer will translate ambiguous research needs into production systems, manage full technical lifecycles, and collaborate directly with researchers, founders, and strategic partners.

Create reinforcement learning environments and golden reference solutions for AI software-engineering tasks. Develop code samples, debug complex defects, implement features, refactor legacy systems, optimize performance, document technical reasoning, and review peer submissions. Work may involve Python, Java, Rust, Go, C++, or TypeScript. The role is remote, part-time contract work with output-based compensation and rapid onboarding.

Design, develop, evaluate, and refine machine learning models using Python and leading ML frameworks. Analyze datasets with MongoDB, build preprocessing and data pipelines, tune hyperparameters, benchmark model performance, document experiments, and provide data-driven recommendations. Collaborate remotely with cross-functional contributors to improve and operationalize models in cloud or enterprise environments.