APIs AI Jobs

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The Sales Enablement Systems Lead is responsible for reinventing how ElevenLabs manages, organizes, and delivers internal knowledge to Sales teams. The role includes auditing and mapping current content across multiple platforms, designing a unified content architecture, creating governance frameworks for content management, and building a single source of truth for sales teams. The lead will own and evolve LMS and internal knowledge systems, evaluate and implement modern content management tools, build integrations connecting content systems to seller workflows, and design personalized learning paths. They will build and deploy AI-powered sales agents, contribute to GTM agent swarm strategy, design agents for seller support, and use ElevenLabs' voice and agent technology for enablement. The role involves defining "Agentic Enablement," building AI coaching tools, and creating systems for auto-updating content. Coordination across Product Marketing, Company Ops, and RevOps for knowledge management alignment is required. Success metrics include tracking content freshness, usage, findability, time-to-answer, building dashboards for system health, and continuously improving content and agent performance. The role also involves documenting best practices and influencing the evolution of the systems function as Sales Enablement scales.

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Own 0→1 and 1→n builds of voice AI systems in real-world environments. Write and ship production-grade code across backend and integrations using languages such as Python and JavaScript. Design and implement custom workflows, automations, and full-stack systems on top of Retell. Work directly with customers to understand problems, prototype solutions, and deploy quickly. Make product and architectural decisions in ambiguous, fast-moving environments. Identify gaps in the platform and build or extend internal tools to solve them. Translate real-world usage into product improvements and new features. Move quickly from idea to prototype to production with minimal oversight.

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Partner deeply with enterprise customers to identify strategic AI use cases, validating technical feasibility and owning the end-to-end implementation of tailored solutions; architect and deliver custom applications, templates, and integrations leveraging WRITER's platform, APIs, and Knowledge Graph capabilities to solve complex business challenges; translate intricate technical concepts and platform capabilities into clear, prescriptive solution recommendations, guiding customers through the generative AI landscape; collaborate relentlessly with internal Product and Engineering teams, providing crucial customer feedback that directly influences the product roadmap and drives continuous innovation; drive down customer time-to-value by developing scalable processes, robust documentation, and efficient workflows for technical integrations; champion the successful adoption and expansion of WRITER's AI solutions within customer accounts, ensuring maximum impact and return on investment.

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As a deployment engineer at WRITER, you will partner deeply with enterprise customers to identify strategic AI use cases, validate technical feasibility, and own the end-to-end implementation of tailored AI solutions. You will architect and deliver custom applications, templates, and integrations leveraging WRITER's platform, APIs, and Knowledge Graph capabilities to solve complex business challenges. You are expected to translate intricate technical concepts and platform capabilities into clear, prescriptive solution recommendations, guiding customers through the generative AI landscape. You will collaborate closely with internal product and engineering teams, providing crucial customer feedback that influences the product roadmap and drives continuous innovation. You will develop scalable processes, robust documentation, and efficient workflows for technical integrations to drive down customer time-to-value. Additionally, you will champion the successful adoption and expansion of WRITER's AI solutions within customer accounts to ensure maximum impact and return on investment.

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The AI Implementation Manager is responsible for owning the delivery and stabilization of Ema's agentic AI solutions from commitment through production rollout and steady state. Responsibilities include end-to-end AI delivery ownership, ensuring solutions align with Ema's agentic architecture and platform capabilities, developing a deep understanding of customer business processes to translate workflows into feasible agentic AI workflows, providing delivery-focused technical oversight to anticipate implementation issues, acting as the primary delivery point of contact for customer business and IT stakeholders, coordinating across Engineering, Product, Data, Infrastructure, and Value Engineering teams, managing delivery under pressure by coaching stakeholders, communicating delivery progress, risks, and decisions clearly, tracking success through adoption signals and outcome-adjacent metrics, and providing day-to-day delivery leadership and mentorship to promote shared standards and delivery discipline.

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The GTM Engineer is responsible for building internal systems that power how the company identifies demand, engages buying groups, accelerates deals, and scales revenue. This includes designing and shipping signal infrastructure, agent workflows, and orchestration tooling that convert GTM data into automated actions such as account intelligence, buying-stage detection, SDR alerts, personalization, and deal acceleration. Specific tasks include building GTM signal infrastructure to score ICP fit, map buying committees, and track engagement across accounts; capturing intent and engagement signals at scale; detecting buying stages and deal health; orchestrating automated GTM actions based on live engagement signals; building internal tooling and agent workflows to automate manual GTM workflows; partnering with marketing, sales, and revenue operations to translate GTM strategy into scalable automation systems; and maintaining data quality and governance across GTM systems.

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Drive strategic technical discovery with Fortune 500 prospects and customers, translating complex business challenges into clear, impactful technical solutions for AI-powered work; architect and design robust, scalable, and secure generative AI solutions for enterprise clients by leveraging WRITER's platform, APIs, and custom applications to solve critical business problems; lead the development and execution of compelling proofs of concept (PoCs) and demonstrations, building custom templates and integrating WRITER's capabilities to showcase transformative value and accelerate time-to-value for customers; serve as a trusted technical advisor to C-suite executives, VPs of Engineering, and AI leaders by guiding their generative AI strategy and collaborating to define enterprise-level architecture roadmaps; partner closely with WRITER's product and engineering teams to provide critical feedback from customer engagements to influence the product roadmap and ensure solutions meet evolving market needs; champion the adoption of WRITER's platform and APIs by educating prospects and partners on the art of the possible with generative AI and empowering them to build their own innovative solutions.

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Build and maintain back end services that handle model inference and user requests, design systems to manage requests, sessions, and streaming responses, implement reliability mechanisms such as rate limiting, retries, and graceful failure, build authentication and access controls for public usage, design systems for logging, telemetry, and evaluation signals, improve latency, throughput, and reliability of model serving, integrate new model checkpoints into the production system, and work closely with training and infrastructure engineers to deploy and operate the model. The role involves working inside production systems including logs, traces, performance profiles, and deployment pipelines to ensure the system stays up, fast, and behaves predictably under load.

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Build and own autonomous, customer-facing agentic systems that directly drive Revenue, Pipeline, and Marketing efficiency. Own end-to-end product execution, from early prototypes to reliable production systems with strong instrumentation and evaluations. Work across the full stack, including APIs, orchestration, data flows, frontend experiences, and deployment. Partner closely with marketing, demand gen, and enterprise sales stakeholders to define success metrics and functional requirements. Apply OpenAI models and tooling in novel ways, making informed tradeoffs between models, platforms, and architectures. Continuously iterate based on live usage, agent behavior, and performance data.

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Lead and grow a high-performing team of 8–9 engineers building reinforcement learning environments. Manage, mentor, and develop senior engineers and future engineering leaders. Partner closely with research, product, and operations teams to define roadmap and execution priorities. Drive technical architecture for scalable, reliable, and extensible environment systems. Build plug-and-play environments that integrate seamlessly with model training pipelines. Balance platform rigor with operational complexity and data quality requirements. Establish engineering best practices around reliability, observability, and performance. Foster a culture of ownership, velocity, and high technical standards.