TypeScript AI Jobs

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Design, build, and operate scalable back-end systems that power AI agent and workflow builders. Own mission-critical services and infrastructure, delivering impactful features from ideation through to production. Push the boundaries of applied AI by enabling new agent capabilities, workflow orchestration, and system behaviours. Shape how engineering is done by influencing standards, architecture, and processes as the company scales. Mentor and support engineers across the team to raise the technical quality and ownership. Set and uphold high standards for code quality, performance, reliability, and security. Collaborate closely with product, design, and leadership to align technical direction with business outcomes.

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Design, build, and operate scalable back-end systems that power real-time, AI-driven chat experiences. Own mission-critical services and infrastructure, delivering impactful features from ideation through to production. Push the boundaries of applied AI by enabling new agent capabilities, workflows, and system behaviours. Shape engineering standards, architecture, and processes as the company scales. Mentor and support engineers across the team, raising the bar for technical quality and ownership. Set and uphold high standards for code quality, performance, reliability, and security. Collaborate closely with product, design, and leadership to align technical direction with business outcomes.

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Design, build, and operate scalable back-end systems that power AI agent and workflow builders. Own mission-critical services and infrastructure, delivering impactful features from ideation through to production. Push the boundaries of applied AI by enabling new agent capabilities, workflow orchestration, and system behaviours. Shape how the engineering team builds by influencing engineering standards, architecture, and processes as the company scales. Mentor and support engineers across the team to raise the bar for technical quality and ownership. Set and uphold high standards for code quality, performance, reliability, and security. Collaborate closely with product, design, and leadership teams to align technical direction with business outcomes.

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Contribute to building Reinforcement Learning Environments (RLE) and supporting infrastructure; implement features across backend systems and frontend interfaces; work with senior engineers to improve system reliability and performance; help build modular workflow domains such as engineering, finance, and legal; support data pipelines that power model training and evaluation.

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Design and implement LLM guardrails that detect abuse scenarios in AI-generated code and agent interactions. Build AI-powered detection systems that use LLMs to identify malicious patterns, classify threats, and automate response decisions. Build and operate abuse detection systems that identify phishing, cryptomining, account takeover, and financial fraud across millions of daily user actions. Design automated response mechanisms that enforce platform policies without manual intervention. Own the full abuse response lifecycle: detection, investigation, enforcement, and handling appeals alongside Support and Legal. Analyze attack patterns using BigQuery and Hex, turning investigation findings into new detection rules. Maintain and extend internal detection tools (Slurper, Netwatch) that continuously monitor user activity. Integrate and tune security scanners (SAST, SCA) in CI pipelines with tight performance SLAs. Track abuse trends, measure detection effectiveness, and adapt defenses as attack patterns evolve.

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Define and drive architecture for scalable, extensible Reinforcement Learning Environments (RLE) systems and data pipelines. Lead development of platform capabilities enabling rapid domain creation. Partner with Research, Product, and Operations to shape strategy and execution. Set standards for reliability, observability, performance, and data quality. Mentor engineers and elevate engineering excellence across the team. Identify and solve systemic bottlenecks in scaling environments and data generation.

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Build and scale core components of Reinforcement Learning Environments (RLE) and infrastructure, design and implement backend systems and data pipelines, translate ambiguous product and research needs into production systems, develop modular, reusable workflow domains, and improve reliability, observability, and system performance.

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The Senior Full Stack Software Engineer is responsible for owning technical delivery end-to-end, shaping the architecture of ML-powered applications, and leading implementation across cloud services, APIs, and modern front-end frameworks with Claude Code, the Claude Agent SDK, and the Claude API integrated into design, building, and shipping processes. They act as the technical backbone of their project pod, balancing hands-on development with technical leadership, architectural decision-making, and client-facing collaboration. Responsibilities include reducing project risk through proactive ownership of epic-level design and execution, improving technical decision-making via research and evaluation of solutions including AI tooling, increasing client confidence by leading technical discovery sessions and acting as a technical SME, leading feature and epic-level implementation, leading architecture and solution design for moderately complex solutions, championing AI engineering best practices within the pod, providing mentorship and technical leadership including code review and hiring assistance, engaging with clients for technical discovery and scoping, and implementing higher-level testing and quality strategies for deployed solutions and LLM-powered features.

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Build and evolve shared AI Platform capabilities that serve as the foundation for teams building with machine learning and generative AI across Zapier. Work mostly in TypeScript and Python, focusing on improving LLM Ops and ML Ops capabilities including observability, monitoring, evaluation, deployment workflows, and operational guardrails. Design and implement systems to measure and improve performance, reliability, safety, and cost efficiency of AI-powered experiences. Identify tooling gaps proactively and work across teams to standardize best practices for building, deploying, and monitoring AI-driven experiences. Collaborate closely with engineers across product, infrastructure, and data teams to ensure AI components are reusable, well-documented, and easy to adopt company-wide. Evaluate emerging tools, models, and patterns in the AI ecosystem and help determine which ones should be incorporated into Zapier’s shared platform.

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You will own features end-to-end, holding a piece of work from contract through rollout across whichever surfaces it needs to touch, including the TypeScript/Node backend, the iOS app, the Android app, or all three. You will deliver the entire feature rather than handing off parts outside your domain. Early tasks include shipping meaningful production code on one of the products, defining and defending API contracts with mobile and design teams before implementation, and iterating on those contracts. You are expected to develop a point of view on the team's workflow and address inefficiencies, working across stack boundaries to deliver complete features and get them in front of real users.