Reconstruct and enhance Microsoft PowerPoint presentations to improve clarity, visual appeal, and impact through effective storytelling. Collaborate virtually to provide feedback and contribute domain-specific insights to support the training and evaluation of AI models.
Design realistic, multi-step HR and people operations tasks with objective grading criteria for AI benchmarking. Create supporting materials such as policy documents, payroll reports, and headcount data to ensure tasks measure actual domain expertise.
Annotate and review clinical content and inpatient documentation to ensure accuracy and clinical soundness. Evaluate AI-generated outputs for clinical safety and appropriateness while collaborating with stakeholders to refine project guidelines.
Review and assess medical records and clinical documentation to provide high-quality input for AI model training. Identify discrepancies in patient assessments and care plans while providing actionable feedback to improve AI performance.
Define and lead the research agenda for Physical AI, focusing on robotics, embodied agents, and scalable data-generation systems. Build and mentor a world-class research team while partnering with leading AI and robotics labs to solve complex physical-world problems.
Review and refine legal citation rubrics while identifying edge cases and ambiguous rules. Create sample cite-checking tasks that include source material, realistic errors, and correct explanations for various difficulty levels.
You will identify, engage, and close partnerships with companies to acquire workflow data for AI training and evaluation. This involves managing the end-to-end partnership process, from initial discovery calls and commercial negotiations to onboarding and pipeline management.
Build reproducible environments and implement reference solutions for data science tasks. Develop evaluation criteria and run coding agents to identify and refine task requirements.
You will create realistic coding tasks and implement deterministic verifiers to help train next-generation AI systems. Your work involves feature implementation, bug fixes, and providing high-quality input to shape how models learn and reason.
You will build and improve AI agent capabilities by developing backend services and experimenting with modern LLMs and orchestration frameworks. You will also collaborate with product teams to deploy scalable AI features and evaluate model performance in production environments.
The Forward Deployed Engineer will partner with AI labs and enterprises to define research goals and implement ML pipelines for data curation and model evaluation. They will also build and maintain agentic systems and production-grade infrastructure to support multi-turn workflows.
You will create reinforcement learning environments to test AI models on complex software engineering tasks like debugging and optimization. Additionally, you will contribute expert-level code samples and validate peer-contributed submissions to improve AI training data quality.
Design, develop, and refine machine learning models using Python while managing data efficiently with MongoDB. Collaborate with cross-functional teams to evaluate model performance, tune hyperparameters, and document methodologies for repeatable workflows.
