JavaScript AI Jobs

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Build end-to-end AI features by architecting and shipping fullstack solutions from React frontends to Python backend services that leverage voice AI and large language models to automate clinical workflows; implement and fine-tune audio processing pipelines ensuring accurate performance of Automatic Speech Recognition (ASR) and LLM agents in diverse medical environments; translate complex clinical feedback into technical solutions by rapidly prototyping and deploying improvements to model behavior, prompting strategies, and audio handling; optimize fullstack performance for real-time audio streaming and token generation to minimize latency for seamless clinician interaction; partner with implementation and clinical teams to shorten the feedback loop by shipping critical integrations and feature requests from concept to production quickly.

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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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Own end-to-end data extraction workflows across complex websites, ensuring complete coverage, accuracy, and reliable delivery of structured datasets. Leverage internal tools (Apify, OpenRouter) alongside custom workflows to accelerate data collection, validation, and task execution while meeting defined requirements. Ensure reliable extraction from dynamic and interactive web sources, adapting approaches as needed to handle JavaScript-rendered content and changing site behavior. Enforce data quality standards through validation checks, cross-source consistency controls, adherence to formatting specifications, and systematic verification prior to delivery. Scale scraping operations for large datasets using efficient batching or parallelization, monitor failures, and maintain stability against minor site structure changes.

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Debug and fix issues in the platform and ship pull requests with fixes. Build internal tools and copilots powered by generative AI to enhance the team. Rapidly prototype proof-of-concepts for customer use cases. Collaborate across Engineering, Product, and Solutions teams to unblock customers and advance AI adoption.

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Build and maintain features for the web-based property management platform using TypeScript, React, Node.js, PostgreSQL, and AWS. Contribute to a monorepo architecture, working within two-week sprint cycles to deliver high-quality code. Implement integrations including DocuSign, Plaid, Stripe, and ownership group payout systems. Optimize platform performance and user experience by replacing legacy systems. Build and integrate AI agents using Claude and other AI APIs to automate organizational processes, developing API integrations and custom agents. Collaborate with the CEO on prioritizing automation opportunities. Take ownership of tasks, independently research and implement solutions to challenges, proactively identify and implement improvements, and contribute ideas to platform architecture and development priorities.

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Design and build AI agents that outperform human agents in managing complex customer interactions and driving customer retention. Identify cross-customer trends that guide the evolution of Decagon’s agent building platform and research efforts. Experiment with and run evaluations on the latest text and voice models, then integrate them at scale with large enterprise-grade customers.

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As a Product Engineer, you will dream up, build, and ship LM Studio features to millions of users worldwide at a fast pace. Your work will intermingle UI development with systems engineering, design, and applied AI/agentic engineering. You are expected to have a holistic understanding of software systems and the ability to work across the stack.

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Create challenging coding test cases that push AI coding systems to their limits. Review and refine realistic coding tasks based on provided production codebases with realistic scope, requirements, and information sources. Write comprehensive functional tests that validate actual end-to-end behavior and edge-cases, not just superficial checks. Craft fair but hard challenges where the AI has all the context it needs but must work for it, involving information scattered across files and external sources and requiring complex reasoning. Analyze AI failures to understand what the model struggles with versus what it masters. Iterate based on feedback from expert QA reviewers who score work on seven quality criteria.

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Create challenging coding test cases that push AI coding systems to their limits by reviewing and refining realistic coding tasks based on provided production codebases with realistic scope, requirements, and information sources. Write comprehensive functional tests that validate actual end-to-end behavior and edge-cases, not just superficial checks. Craft "fair but hard" challenges where the AI has all the context it needs but must work for it, involving information scattered across files and external sources and requiring complex reasoning. Analyze AI failures to understand areas where the model struggles versus what it masters. Iterate based on feedback from expert QA reviewers who score the work on seven quality criteria.

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Create challenging coding test cases that push AI coding systems to their limits by reviewing and refining realistic coding tasks based on provided production codebases with realistic scope, requirements, and information sources. Write comprehensive functional tests that validate actual end-to-end behavior and edge cases, not just superficial checks. Craft "fair but hard" challenges where the AI has all the context it needs but must work for it, involving information scattered across files and external sources and complex reasoning. Analyze AI failures to understand what the model struggles with versus what it masters. Iterate based on feedback from expert QA reviewers who score work on seven quality criteria.