JavaScript AI Jobs

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Ship critical infrastructure 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 the company's value to customers, pushing back on requirements if a better, faster solution is seen. Demonstrate full-stack proficiency by working across system boundaries, including frontend UX, LLM agents, database schema, and event infrastructures. Leverage AI tools to automate boilerplate so focus can be on quality, architecture, and product taste. Constantly raise the velocity bar by optimizing development loops, refactoring legacy patterns, automating workflows, and fixing broken processes.

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Build and scale reinforcement learning environments and platforms behind them; drive architecture for scalable, reliable, extensible environment systems and data generation pipelines; partner with Research, Product, and Ops teams to turn ambiguous needs into production systems; build modular, plug-and-play domains that integrate cleanly with training and evaluation loops; improve reliability, observability, performance, and data quality of systems.

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The AI Deployment Engineering Manager leads the AI Deployment Engineering team in the Digital Native segment, focusing on ensuring the safe and effective deployment of Generative AI applications for developers and enterprises. Responsibilities include owning the strategy and operating model of the team to align with company objectives and customer needs, leading, building, and mentoring the team to deliver exceptional customer outcomes evidenced by production customer applications and increased API adoption. The role involves serving as the technical advocate for customers by synthesizing their needs to guide Research and Applied Product/Engineering roadmaps. The manager acts as the primary technical escalation point during development, maintaining direct communication with executive-level stakeholders and fostering trust. Additionally, the role requires serving as an industry thought leader and championing the safe and innovative application of the technology across various sectors. The manager oversees the entire implementation journey for strategic technology and software customers in the Americas, ensuring seamless platform integration, aligning technical teams to deliver a consistent and exceptional experience throughout the customer lifecycle, with success measured by live production applications, increased API adoption, and impactful customer stories.

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Lead, mentor, and grow a team of AI Deployment Engineers supporting strategic AWS partner engagements and customer deployments. Define the operating model, engagement strategy, and technical priorities for the AWS Partner ADE pod. Partner closely with AWS partner leadership, solution architects, delivery organizations, and customer stakeholders to accelerate production adoption of OpenAI technologies. Guide teams through complex generative AI and traditional ML deployments, including architecture reviews, implementation planning, security considerations, evaluation strategies, and operational readiness. Serve as a senior technical escalation point for critical partner and customer engagements. Collaborate with Product, Research, and Engineering teams to synthesize partner feedback into platform improvements, tooling enhancements, and deployment best practices. Develop scalable enablement frameworks, reference architectures, and repeatable deployment patterns to improve partner effectiveness and reduce time-to-production. Drive operational excellence including resource planning, prioritization, hiring, onboarding, performance management, and career development. Act as an external thought leader on enterprise AI deployment, cloud-native AI architectures, and responsible AI adoption within the AWS ecosystem.

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In this role, you will build the next set of AI Features at Pylon, rapidly iterating based on customer feedback, and improve the quality and performance of AI features.

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Build the platforms that power Pylon's AI features such as prompt executions and search infrastructure. Improve LLM observability including AI evaluations both online and offline, scorers, and prepare Pylon's AI for future scaling. Enhance the quality and performance of AI features.

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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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You will explore and build experimental AI experiences that help define how people interact with proactive AI systems, working at the intersection of design, engineering, and AI experimentation. Responsibilities include prototyping experimental AI interfaces beyond standard chat UI, building quick proof-of-concept experiences using AI models, APIs, and frontend tools, exploring new ways for users to understand, direct, and collaborate with AI systems, testing interaction ideas around human control, trust, transparency, memory, multimodal input, and long-running workflows, working with product, design, and ML teams to turn early ideas into testable prototypes quickly, identifying useful HCI patterns from research, products, and emerging tools to test their applicability for A1, documenting experiments clearly by detailing what was tested, what worked, what failed, and suggesting what should be built next, and contributing to the team's understanding of what HCI should look and feel like in a real consumer app.

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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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As a Senior Founding GTM Engineer at Ambient.ai, you will architect AI-driven go-to-market (GTM) workflows and automate end-to-end processes using tools like Clay, AirOps, and n8n. You will build and maintain systems that support AI agents used for account prioritization, persona identification, and personalized messaging. You will operationalize intent signals through workflows that synthesize first- and third-party intent signals to launch hyper-targeted, data-driven ABM campaigns. Responsibilities include writing scripting and "glue-code" in Python or JavaScript to ensure clean data flow between Salesforce, HubSpot, Snowflake, and the AI toolchain. You will build and iterate on messaging workflows using prompt engineering and model context protocols, lead design sprints to optimize reusability and outcome predictability, define KPIs, monitor system performance, and make data-driven improvements to the GTM stack. Additionally, you will document system behavior, contribute to shared prompt libraries, and ensure that Sales and Marketing teams understand how to leverage the AI system. The role involves mapping workflows, identifying data silos, delivering system audits, building and testing agent-level workflows, and leading AI-driven GTM infrastructure ownership including observability and agent governance practices.