React AI Jobs

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Design and build intuitive web interfaces for robot data annotation, datasets visualization, and experiment tracking. Utilize data-driven techniques to optimize interfaces for efficiency and fast iteration cycles. Integrate AI models to automate manual tasks. Work together with AI researchers, robot operators, and annotators to support new user experiences.

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Work closely with the Product Lead as a Mid-level or Senior Fullstack Engineer operating at the intersection of core product development and clinical application, building end-to-end AI features including architecting and shipping fullstack solutions from React frontends to Python backend services that leverage voice AI and LLMs to automate clinical workflows. Implement and fine-tune audio processing pipelines to ensure Automatic Speech Recognition (ASR) and LLM agents perform accurately in diverse, real-world medical environments. Translate complex clinician feedback into technical solutions, rapidly prototyping and deploying improvements to model behavior, prompting strategies, and audio handling. Optimize fullstack performance to handle real-time audio streaming and token generation to minimize latency and create seamless conversational experiences for clinicians. Partner with implementation and clinical teams to quickly ship critical integrations and feature requests from concept to production in days rather than quarters.

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As a Lead Engineer at Eloquent AI, you will lead the development of AI-powered full-stack applications while overseeing and mentoring other engineers. You will remain hands-on across the stack, take ownership of technical direction, code quality, and delivery standards. Responsibilities include designing and building full-stack applications that power AI-driven workflows for enterprise users, overseeing and reviewing the work of other engineers to ensure high-quality, production-ready code, providing technical guidance, architectural direction, and hands-on support where needed, developing high-performance front-end interfaces for AI agent control, monitoring, and visualization, building scalable backend services that support real-time AI interactions, knowledge retrieval, and automation, working closely with AI researchers and ML engineers to integrate LLMs, RAG, and automation into production-ready systems, establishing engineering best practices across testing, deployment, and performance optimisation, and continuously iterating and refining AI-driven products balancing speed with robustness.

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As a Full-Stack Product Engineer at Ideogram, you will build products that integrate generative AI directly for creators, working across the entire technology stack to create delightful user experiences and optimize backend systems that serve millions. Responsibilities include shipping features that users love, combining product intuition with strong ownership, user empathy, and the ability to move quickly within an evolving AI landscape. You will design APIs and data models to support changing product needs, use AI-native engineering tools to accelerate development and debugging, and take initiative to identify and complete opportunities. The role involves working with minimal direction in a fast-paced environment, taking responsibility for outcomes, collaborating, communicating technical concepts to diverse stakeholders, seeking feedback, pushing for quality in code review, and mentoring others.

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Lead the deployment, performance, and reliability of AI agents operating in live, high-stakes healthcare environments. Architect and scale full-stack systems integrating with EHRs, legacy healthcare platforms, and real-time voice infrastructure. Drive technical strategy for customer-facing solutions, collaborating with product and engineering to translate client needs into scalable architecture. Mentor engineers and set standards for code quality, testing, security, and HIPAA-compliant development. Shape product roadmap by identifying systemic challenges in customer workflows and proposing high-leverage technical solutions.

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Design and deliver production-grade AI agents for healthcare that handle sensitive patient and member interactions while maintaining strict HIPAA compliance. Drive the Agent Development Life Cycle with ownership from pilot through deployment and iteration. Partner with healthcare leaders to understand challenges and build AI agents that address operational needs. Develop expertise in healthcare systems, workflows, and data/privacy standards to create trustworthy AI experiences. Guide and contribute to the evolution of Sierra's core platform based on customer feedback. Examples of projects include building AI agents for insurance networks, providers, primary and urgent care clinics, and healthcare financial platforms, as well as experimenting with voice models for secure interactions.

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Lead development of Agent Memory, the system that enables AI agents to remember and personalize interactions across conversations, transforming one-off exchanges into continuous relationships that drive measurable business outcomes at scale. Balance competing stakeholder needs including end users expecting personalization, operations teams requiring compliance, and developers needing flexible integration patterns. Define how AI agents should remember, addressing session continuity, long-term relationships, intelligent consolidation, context retrieval, and privacy-preserving personalization. Collaborate closely with engineering teams on distributed systems, ML teams on retrieval and embedding technologies, and infrastructure teams on scalable storage solutions. Serve as a trusted and strategic advisor to customers in partnership with sales, go-to-market, and forward-deployed teams. Lead the product through all stages from concept to execution in collaboration with cross-functional partners.

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As a Forward Deployed Software Engineer at Northslope, you will be responsible for helping customers solve their most valuable problems by working closely with stakeholders to conceive, architect, build, and maintain applications that have a measurable impact on their business. You will understand customers' most critical objectives and build operationalized workflows on their enterprise data that end users actually use to make change within the business. Your role involves iterating quickly, building from scratch, and helping organizations transform their operations at an uncommon speed. You will not only ship code but also help reshape how entire organizations work by putting powerful tools in the hands of executives and real operators.

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As Head of Engineering at Adthena, you will be responsible for the technical execution behind a platform that processes vast volumes of search data, runs complex distributed computations, and delivers enterprise-grade insights. You will own the engineering roadmap execution for all products and platform capabilities, join architectural discussions concerning distributed data processing, ML pipelines, and web application architecture, and ensure the systems are scalable and reliable. You will champion engineering best practices, establish predictable delivery processes across all teams, implement measurable engineering KPIs, and drive continuous improvement in release quality. Additionally, you will lead and mentor direct reports, build an engineering organization that scales, foster a collaborative environment, partner with Product, Data Science, ML, and DevOps teams, contribute to the company-wide technology strategy, identify opportunities to accelerate innovation, and manage engineering budgets, tooling spend, cloud infrastructure costs, and vendor relationships.

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As a Full Stack Software Engineer at Replicant, you will design and deliver technology that powers natural, human-like conversations at scale to help companies reduce wait times, improve customer satisfaction, and empower representatives to focus on complex problems. You will build rich user experiences and backend services that enable customers to design, launch, and monitor AI-powered conversations. Responsibilities include building new features for Replicant's core AI voice and chat products handling millions of daily conversations, shipping full stack end-to-end features quickly, integrating automatic speech recognition, text to speech, and conversational AI model improvements into products, refactoring, optimizing, and debugging production systems balancing latency, cost, and user experience, participating in regular on-call rotations monitoring live systems, continuously improving systems based on performance metrics and customer feedback, shaping a culture emphasizing knowledge sharing and mentorship across distributed systems and enterprise-scale AI design, and participating in team and company-wide office events with travel required.