TypeScript AI Jobs

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As the first US based Engineer, the role entails acting as the technical bridge between the product vision and customer reality. Responsibilities include designing, architecting, and shipping full-stack features that solve customer compliance challenges, owning the technical relationship with key customers by implementing solutions, gathering requirements, and translating feedback into product improvements. The engineer will build scalable services and APIs for the LLM compliance platform, make high-impact technical decisions quickly while being accountable to engineering standards and customers, challenge assumptions about what and how to build, and shape the product roadmap and engineering practices as the company scales from Series A to market leadership. The work combines coding, customer engagement, and steering product direction with high autonomy and collaboration with the founding team.

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Design and deploy production-grade agents using LangGraph and LangSmith that handle technical support queries, troubleshoot integrations, and guide users through complex onboarding flows. Analyze customer friction points to build self-service AI systems that reduce support volume and improve customer experience. Act as the product owner and technical lead to proactively identify opportunities for improvement, propose architectures, and own the full lifecycle of the systems built. Participate in the feedback loop for the product team by identifying gaps in frameworks and contributing to the LangChain and LangGraph open-source ecosystem. Develop AI-native onboarding workflows that automate documentation retrieval and code generation to help enterprise customers move from prototypes to production faster.

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

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Design, architect, and operate scalable services and APIs that power the LLM compliance platform. Architect how AI insights are surfaced to users, ensuring the system is robust, fast, and intuitive. Make high-impact technical decisions quickly. Challenge "why" and "how" to ensure delivery of the best possible experience for users. Shape engineering culture, standards, and tooling as the company grows. Own end-to-end technical decisions including designing systems, architecting solutions, shipping to production, and iterating based on customer feedback.

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Work on a small, high-caliber team building AI products for clients, from requirements gathering and prototyping through system design, development, testing, and deployment. Own features end-to-end and develop domain expertise across a range of AI use cases. Spend most of the time coding and frequently interact with clients to ensure the solutions meet their needs.

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You will work on a small, high-caliber team building AI products for clients, setting technical direction, writing code, and serving as the go-to person when challenges arise. Spend approximately 75% of your time coding and 25% interacting with clients, including CTOs, to understand problems, evaluate tradeoffs, and ensure solutions meet their needs.

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Work on projects such as developing a product that root causes KTLO work and recommends solutions, building a software catalog that works for monoliths and is user-friendly, and helping protect engineering focus time by systemically solving sources of distraction or mental load with AI.

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As a Software Engineer on the Coding Pod, you will build the data infrastructure and pipelines that power frontier AI coding models. Responsibilities include designing and building scalable data pipelines for generating, transforming, and validating large-scale coding datasets; developing systems for task generation, dataset curation, and quality assurance, including automated and human-in-the-loop evaluation workflows; integrating with developer ecosystems such as GitHub and building tooling to support real-world coding environments; working with containerized environments like Docker to safely execute and evaluate code at scale; building backend systems and APIs that power dataset delivery and model evaluation pipelines; collaborating closely with ML researchers, product managers, and other engineers to define evaluation methodologies and improve dataset quality; implementing automated grading, benchmarking, and assessment systems for coding tasks; debugging and optimizing pipeline performance, reliability, and scalability across distributed systems; and contributing to architectural decisions around data infrastructure, evaluation systems, and pipeline orchestration.

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Debug and fix issues in the platform and ship PRs 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 drive AI adoption.

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Ship critical infrastructure by managing real-world logistics and financial data for the largest enterprise in the world. Own the why by building deep context through customer calls and understanding Loop’s value to customers, pushing back on requirements if a better, faster solution exists. Work across system boundaries with full-stack proficiency, including frontend UX, LLM agents, database schema, and event infrastructures. Leverage AI tools to automate boilerplate work, focusing on quality, architecture, and product taste. Constantly optimize development loops, refactor legacy patterns, automate workflows, and fix broken processes to raise the velocity bar.