AI Full-Stack Engineer Jobs

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As a Full-Stack Product Engineer at Ideogram, you will build products that bring generative AI directly to creators, working across the entire technology stack from designing user experiences to optimizing backend systems that serve millions. Your focus will be on shipping features that users love by combining product intuition, strong ownership, and user empathy. You will design APIs and data models to support evolving product needs, utilize AI-native engineering tools to speed up development, debugging, and understanding of the codebase, and work effectively across frontend and backend systems. You will also be responsible for explaining technical concepts to both technical and non-technical stakeholders, participating in constructive code reviews, collaborating with the team, and taking full responsibility for the outcomes of your work, not just the code.

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Build end-to-end product features across frontend, backend, and AI integrations; design agent workflows that handle planning, tool use, failure, and recovery across multiple steps; integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions; design real-time AI interactions with streaming, partial results, and tight latency constraints; improve system reliability, observability, and fallback mechanisms; collaborate closely with ML, backend, and product teams to ship features end-to-end; continuously iterate based on real usage and failure modes.

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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 support 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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Design, build, and ship full stack features across the Fuse lending platform including backend APIs and services and customer-facing dashboards and operator tools. Model and configure each client's lending domain including loan types, underwriting rules, debt adjustment rules, task flows, adverse actions, and email/e-sign templates. Build and maintain client-specific automations and workflows that orchestrate the end-to-end loan lifecycle integrating with external services such as credit bureaus, KYC providers, banking rails, and document services. Ship AI-powered features such as LLM-based automations, classification and extraction pipelines, agentic workflows, and tool-using agents that operate on real customer data. Own features end-to-end including scoping, implementation, testing, deployment, monitoring, and iteration based on production signals. Write clean, well-tested, observable code and contribute to shared standards on code quality, reliability, and security.

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As a Software Engineer, Core Science, you will help design and build the platforms, products, and infrastructure powering AI-native scientific research workflows inside Codex. You will own systems end-to-end from architecture and implementation to evaluation, launch, and production operations, focusing on quality and velocity. Responsibilities include building backend services, data and orchestration pipelines, model-powered workflows, and user-facing experiences. You will shape AI-powered scientific research by building backend systems and full-stack product experiences across Prism and Codex. Develop AI-native workflows for paper writing, literature review, paper understanding, research synthesis, and scientific knowledge exploration. Partner closely with researchers and domain experts across biology and adjacent scientific disciplines to understand research workflows, identify AI opportunities, and translate scientific needs into product capabilities. Build AI-powered tools for scientific data analysis and simulation to help researchers explore data, run experiments, interpret results, and iterate on models or hypotheses. Assist in establishing product, platform, and engineering foundations for fast-moving 0 to 1 efforts, balancing rapid experimentation with rigor and reliability required for research tools.

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The role involves implementing full stack features end-to-end across front-end, back-end, and cloud infrastructure layers, building and integrating RESTful APIs and cloud-hosted services primarily on Azure, developing front-end components using modern JavaScript/TypeScript frameworks, writing unit, integration, and API tests, using Docker for local development and containerized deployments, and managing work in Git with effective collaboration with AI agents. The engineer will build features that incorporate large language model (LLM) calls via the Claude API or Azure OpenAI, implement retrieval-augmented generation (RAG) components and tool integrations, write evaluation harnesses for LLM-powered features, and document LLM feature behavior clearly. They will participate actively in technical design discussions, use Claude and AI-assisted development tools for prototyping, code generation, and debugging, write clear prompts and specs for AI agents, review AI-generated code critically, and contribute to the development of AI-assisted workflows. Collaboration includes participating in code reviews, working closely with ML engineers, data engineers, and product managers, contributing reusable components to shared libraries, engaging in sprint ceremonies, and proactively seeking feedback to grow technically and toward leadership.

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As the first US based Forward Deployed Engineer at Haast, you will act as the technical bridge between the product vision and customer needs. You will work as a full-stack engineer responsible for designing, architecting, and shipping full-stack features that solve customers' compliance challenges. Your role involves ownership of end-to-end technical decisions, including designing systems, shipping to production, and iterating on features based on direct customer interaction. You will maintain the technical relationship with key customers by implementing solutions, gathering requirements, and translating feedback into product improvements. Additionally, you will build scalable services and APIs for the LLM compliance platform with a focus on customer experience, make high-impact technical decisions aligned with engineering standards and customer needs, question assumptions about product development, and influence the product roadmap and engineering practices as the company scales from Series A to market leadership.

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Design and develop robust, scalable, and secure generative AI services and applications using Python and modern frameworks to drive enterprise-wide transformation. Build and optimize high-performance, low-latency APIs and microservices for integrating advanced AI models and agentic workflows into the platform. Collaborate closely with product managers, data scientists, and cross-functional engineering teams to translate complex business needs into innovative AI solutions, from concept to production. Implement and maintain responsive user interfaces using technologies like React and TypeScript, primarily focused on backend enablement but including some frontend interaction. Partner with DevOps teams on continuous deployment, logging, and monitoring to ensure top-tier performance and reliability. Own key architectural components, ensuring best practices in code quality, security, and maintainability through rigorous testing and peer reviews.

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Design and develop robust, scalable, and secure generative AI services and applications using Python and modern frameworks to drive enterprise-wide transformation. Build and optimize high-performance, low-latency APIs and microservices for integrating advanced AI models and agentic workflows into the platform. Collaborate closely with product managers, data scientists, and cross-functional engineering teams to translate complex business needs into innovative AI solutions, from concept to production. Implement and maintain responsive user interfaces primarily focused on backend enablement, using technologies like React and TypeScript to deliver intuitive user experiences. Partner with DevOps teams building continuous deployment, logging and monitoring to ensure top-tier performance and reliability. Own key architectural components, ensuring best practices in code quality, security, and maintainability through rigorous testing and peer reviews.

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Design, implement, and maintain scalable, secure agent-driven services and systems that autonomously accomplish tasks using modern AI frameworks. Develop and enhance robust infrastructure and high-throughput APIs, focusing on core agent capabilities such as memory, communication channels, skills, intelligent decision logic, security, and workflow management. Integrate agent capabilities with backend services, data stores, vector databases, search/retrieval systems, and external APIs. Collaborate with product managers, AI researchers, data engineers, and UX teams to translate high-level agent use cases into robust, production-ready software. Ensure reliability, monitoring, and observability for all agent components including metrics, logging, CI/CD, and fault tolerance. Contribute to architectural design decisions and participate in rigorous code reviews to uphold quality and maintainability.