TypeScript 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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The Senior Software Engineer will build a powerful project innovating customer support by defining what an AI-first SaaS product looks like, addressing unique UI/UX, capabilities, and data model challenges of an AI-first company. They will lead ambitious and ambiguous projects involving strong technical decision-making, effective implementation, and incorporate product and design instincts. The engineer will work across the tech stack, collaborate with a top-caliber team, and mentor or lead less experienced engineers. They will participate in an engineering-led culture where everyone owns working with users and building a great product, taking ownership of challenging problems and defining and implementing solutions.

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Design a collaborative "Multiplayer Computer" that lets humans and AI agents work together on shared shells, filesystems, and state—conflict-free and in real time; build high-throughput backend applications and services; create tooling that helps AI systems minimize mistakes through static analysis and deterministic techniques; develop infrastructure (frontend & backend) that empowers product engineers to rapidly ship delightful user experiences; support sophisticated user interfaces, including terminals, code editors, window-management systems, and innovative experiences that require both creativity and algorithmic skill; and bridge the gap between prompt engineers and frontend engineers. Telecommuting is permitted with in-office presence required three times a week (Monday, Wednesday, Friday), with only incidental domestic travel required.

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Build systems that integrate with the EHRs used in American healthcare to make Heidi feel like a native capability rather than a plugin. Develop systems that simplify the complexity of US healthcare billing, compliance, and payer constraints so clinicians do not have to manage these complexities. Write clean, testable code with strong interfaces, error handling, and observability, ensuring the workflows are reliable for clinicians, operators, and downstream systems. Focus on outcomes by ensuring that the built systems help clinicians and improve practice revenue. Create agentic workflow functionalities where AI assists with extraction, reconciliation, and drafting within workflows, incorporating human review, auditability, and control. Collaborate closely in a team environment with frequent pairing and shared ownership of design and implementation. Learn about healthcare organizational operations, especially those serving US customers, to translate requirements and constraints into product improvements.

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The Director of Forward Deployed Engineering will own the Forward Deployed Engineering program end-to-end, including building the team, defining the operating model, and ensuring top strategic accounts feel prioritized. Responsibilities include building, hiring, and managing a team of software engineers and managers deployed into strategic accounts; defining staffing models, engagement structures, and capacity allocation across accounts; developing specialist pods of engineers for new verticals; setting and upholding quality standards for client deliverables, documentation, and knowledge transfer. The role also requires maintaining deep technical fluency to scope custom builds, unblock engineering decisions, and evaluate solution quality; overseeing the design and implementation of tailored workflows, retrieval systems, agent tools, and knowledge sources on Harvey's platform; and ensuring solutions are operationalized with evaluations, documentation, and user training. Additionally, the Director will identify patterns across client engagements to inform product and engineering leadership about client needs and product opportunities with specificity.

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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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The Head of Engineering at Lazo is responsible for owning the technology strategy and roadmap aligned with business and product OKRs, defining the reference architecture for agentic systems including LLMs and tool orchestration, establishing security and compliance baselines such as IAM, data privacy, and SOC2-readiness, and managing cost governance (FinOps). They present trade-offs, risks, and progress in leadership reviews. The role involves hands-on engineering and delivery, including shipping backend services in Python/TypeScript, orchestrating agents and toolchains, integrating external APIs and databases, building robust pipelines, and handling end-to-end DevOps using AWS/GCP, containerization, IaC, CI/CD, and observability, as well as on-call design. They work to reduce technical debt, improve latency and throughput, and manage infrastructure cost per workflow/client. Responsibilities also include defining SLOs and error budgets to reduce MTTR and change-fail rates, implementing data access policies and secure data flows for AI features, driving post-mortems and preventive engineering, hiring and mentoring engineers, setting performance scorecards, fostering a culture of thoughtful trade-offs and fast feedback loops, partnering with Product and AI teams for scalable solutions, collaborating with Ops, Growth, and Customer teams for reliability and launch readiness, and managing key vendors and build-versus-buy decisions with ROI narratives.

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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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Build systems that integrate seamlessly with the EHRs used in American healthcare to feel like a native capability rather than a plugin. Develop systems that handle complex US healthcare billing rules, compliance requirements, and payer constraints so that clinicians do not have to manage these complexities. Write clean, testable code with strong interfaces, error handling, and observability for workflows depended on by clinicians and operators. Focus on outcomes that help clinicians and improve practice revenue, not just code functionality. Create AI-assisted workflow functionality that supports extraction, reconciliation, and drafting with human review, auditability, and clear controls. Collaborate closely with others through frequent pairing and shared design and implementation ownership. Learn about healthcare organizations' practical operations and US customer requirements to guide product improvements.

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Design and deliver production-grade AI agents that are highly performant, reliable, and intuitive, driving revenue directly to Sierra's growth. Own and manage the Agent Development Life Cycle (ADLC) with complete autonomy from initial pilot through deployment and continuous iteration, including building, tuning, and evolving AI agents in production environments. Partner with large enterprises and startups to understand business challenges and build AI agents that transform their operations at scale. Build the future of Sierra's core platform by surfacing unmet customer needs, prototyping new tools and features, and collaborating with research, product, and platform teams to shape AI agent development and Sierra's product.