The Technical Training & Quality Manager will lead technical training, capability development, and quality assurance for AI data programs. They will collaborate with cross-functional teams to ensure data annotation and model evaluation workflows meet high-quality standards.
The Research Data Scientist will conduct independent and collaborative research in Generative AI, LLMs, and NLP to develop evaluation frameworks and analytical prototypes. They will also translate complex research findings into actionable AI/ML solutions while collaborating with cross-functional teams to improve model performance.
The Technical Delivery Manager will own end-to-end program management for AI/ML engagements, serving as the primary point of contact for clients and internal stakeholders. They are responsible for orchestrating delivery roadmaps, managing risks, and ensuring alignment between client expectations and technical execution.
The evaluator will review and score AI-generated customer interactions across various dimensions including faithfulness, tone, and compliance. They will also benchmark customer queries against authoritative business sources to ensure accuracy and quality.
The evaluator will score AI-generated customer interactions for accuracy, tone, and compliance against established rubrics. They will also perform intent and fact verification by benchmarking queries against authoritative business knowledge bases.
The Sales Development Representative will generate and qualify meetings at enterprise accounts within the federal practice by identifying key buyers and funding sources. They are responsible for outbound engagement, account mapping, and maintaining accurate records in Salesforce to support the enterprise sales team.
The Account Executive will manage enterprise accounts within the federal sector, focusing on selling data engineering and AI evaluation services. They are responsible for identifying buying centers, securing paid engagements, and maintaining long-term customer relationships.
The role involves leading end-to-end analytics initiatives, setting standards for data models and pipelines, and partnering with Data Science and Product teams to solve complex monetization problems. You will also be responsible for architecting production-grade SQL pipelines and enabling self-serve analytics through high-quality dashboards.
The role involves leading end-to-end analytics initiatives, architecting production-grade SQL pipelines, and setting standards for data models and dashboards. You will serve as a senior technical leader and thought partner to Data Science and Product teams to solve complex monetization and revenue data problems.
The role involves evaluating and labeling AI model outputs to improve language and reasoning capabilities. Responsibilities include creating training data, ranking content, and performing tasks like summarization and translation to support large language model development.
The role involves wearing a head-mounted camera to record everyday household tasks to help train robotics and AI systems. Responsibilities include following scripted instructions, maintaining consistent procedures, and reporting technical issues with equipment.
The Strategic Account Manager will serve as the primary point of contact for clients, managing relationships and ensuring high levels of satisfaction. They will coordinate with internal technical and operations teams to deliver projects, manage budgets, and shape service roadmaps based on client needs.
The role involves evaluating and labeling AI model outputs to improve language and reasoning capabilities. Responsibilities include creating training data, generating prompts, and performing quality assessments on various digital content types.
Design and implement technical pipelines for LLM post-training, evaluation, and model improvement. Collaborate with research scientists and clients to build robust infrastructure for reproducible experimentation and model assessment.
Lead research and experimentation on LLM evaluation and post-training methods to drive measurable model improvements. Design rigorous evaluation frameworks, benchmark datasets, and scoring methodologies for both LLM and multimodal systems.
Design and validate high-quality datasets for training and evaluating financial-domain LLMs and AI agents. Translate complex financial goals into concrete dataset specifications, rubrics, and statistical evidence to support model risk management.
The role involves designing and validating high-quality datasets and evaluation frameworks for healthcare-specific generative AI models. You will translate clinical goals into measurable specifications and ensure the clinical validity and safety of AI outputs across multimodal health data.
Lead technical discovery and design end-to-end post-training solutions, including SFT data curation and evaluation frameworks, for high-profile AI clients. Act as the primary technical advisor bridging the gap between customer goals and internal research and engineering teams.
The role involves rating AI model performance, labeling content, and creating prompts to improve LLM reasoning and language capabilities. Responsibilities also include generating training data, rewriting text for clarity, and compiling digital content for model fine-tuning.
Design and validate high-quality datasets used to train and evaluate financial-domain LLMs and multimodal AI agents. Translate complex financial goals into concrete dataset specifications, rubrics, and statistical evidence to support model risk management.
Design and validate high-quality datasets and evaluation frameworks for health-domain AI models to ensure clinical accuracy and safety. Collaborate with cross-functional teams to translate clinical goals into measurable dataset specifications and statistical checks.
The role involves rating AI model performance, labeling content, and creating training data to improve LLM reasoning and language capabilities. Responsibilities include generating prompts, rewriting text for clarity, and compiling diverse datasets for model fine-tuning.
The role involves rating AI model performance, labeling content, and creating training data to improve LLM reasoning and language capabilities. Responsibilities include generating prompts, rewriting text for clarity, and compiling diverse datasets for model fine-tuning.
Lead technical discovery and design end-to-end post-training solutions, including fine-tuning pipelines and evaluation frameworks for LLMs. Act as the primary technical advisor for high-profile clients, translating complex AI goals into deliverable engagements and feeding customer insights back into R&D.
Design and implement technical pipelines for LLM post-training, evaluation, and model improvement. Collaborate with research scientists and clients to translate evaluation findings into measurable model enhancements.
The role involves rating, labeling, and ranking AI model outputs to improve the reasoning and language capabilities of LLMs. Responsibilities include creating prompts, generating training data, and rewriting text for clarity and adherence to guidelines.
The role involves rating AI model performance, labeling data, and creating prompts to improve the reasoning and language capabilities of LLMs. Responsibilities also include generating training data through text transformation, summarization, and translation.
The role involves rating AI model performance, labeling content, and creating training data to improve LLM reasoning and language capabilities. Responsibilities include generating prompts, rewriting text for clarity, and compiling diverse datasets for machine learning.
The specialist will rate AI model performance, label content, and create training data to improve LLM reasoning and language capabilities. Responsibilities include generating prompts, rewriting text for clarity, and compiling diverse datasets for model fine-tuning.
The role involves rating AI model performance, labeling content, and creating prompts to train large language models. Responsibilities also include generating training data through text transformation, summarization, and translation.
The role involves rating AI model performance, labeling content, and creating training data through prompts and text transformations. Responsibilities also include rewriting text for clarity and converting audio or language content to improve LLM reasoning.
The role involves rating AI model performance, labeling content, and creating prompts to improve LLM reasoning and language capabilities. Responsibilities also include generating training data through text transformation, summarization, and translation.
The role involves rating AI model performance, labeling content, and creating training data to improve LLM reasoning and language capabilities. Responsibilities include generating prompts, rewriting text for clarity, and compiling diverse datasets for model fine-tuning.
The role involves rating AI model performance, labeling content, and creating training data to improve LLM reasoning and language skills. Responsibilities include generating prompts, rewriting text for clarity, and compiling diverse datasets for model fine-tuning.
The role involves rating AI model performance, labeling content, and creating training data to improve LLM reasoning and language capabilities. Responsibilities include generating prompts, rewriting text for clarity, and compiling diverse datasets for model fine-tuning.
Design and implement robotics data capture solutions and technical pilots to help customers build Physical AI systems. Act as a technical partner to bridge the gap between customer requirements and the delivery of high-quality robotics datasets.
The role involves rating AI model performance, labeling content, and creating training data through prompts and text transformations. Responsibilities also include rewriting text for clarity and converting audio or language to improve LLM reasoning and quality.
Lead research and experimentation on LLM evaluation and post-training methods to drive model improvement. Design rigorous frameworks, benchmark datasets, and scoring methodologies for multimodal and large language models.
The Product Manager will lead a cross-functional team through a simulated product development workflow, defining requirements and driving feature prioritization. They are responsible for facilitating team discussions, managing documentation, and ensuring alignment on project goals and timelines.
The designer will create mockups, flows, and design concepts while collaborating with product and engineering teams to refine user experiences. They will also present design decisions and iterate based on feedback to support AI-driven processes.
The role involves evaluating, annotating, and classifying AI model outputs to improve language and reasoning capabilities. Specialists will also perform data augmentation, prompt generation, and content quality assessments to support the development of large language models.
