APIs AI Jobs

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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.

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As a Forward Deployed Engineer at Dust, your responsibilities include writing production-quality code to build custom integrations, APIs, and tooling for enterprise customers where off-the-shelf solutions are insufficient. You will contribute features and improvements directly to the Dust platform based on customer requirements and field insights. You act as a key cross-functional partner by collaborating with Sales to help onboard customers and with Customer Success to ensure users maximize the value of Dust. You help set the product roadmap by surfacing feedback and insights from customers, partnering with Design and Engineering. You lead demo calls, communicate Dust's value proposition to buyers and evaluators, and act as a trusted advisor to strategic customers by helping set up their Dust workspace, data connections, AI assistants, and workflows. You identify and highlight successful use cases and craft content to help users maximize Dust's value. Additionally, you lead workshops and training sessions to demonstrate advanced features and facilitate customer access to advanced use-cases through Dust's Developer platform and API.

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As a Product Engineer on the Marketing Innovation team, you will build and own autonomous, customer-facing agentic systems that interface directly with enterprise customers, prospects, and revenue-critical workflows. You will partner closely with functional leaders across scaled revenue, demand generation, and marketing to understand desired outcomes and translate those needs into production-grade systems. Responsibilities include building autonomous and semi-autonomous customer-facing and internal agentic systems that drive revenue, pipeline, and marketing efficiency; owning end-to-end product execution from prototypes to reliable production systems with strong instrumentation and evaluations; working across the full stack including APIs, orchestration, data flows, frontend experiences, and deployment; partnering closely with marketing, demand generation, and enterprise sales stakeholders to define success metrics and functional requirements; applying OpenAI models and tooling innovatively, making informed tradeoffs between models, platforms, and architectures; and continuously iterating based on live usage, agent behavior, and performance data.

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The Senior Backend Engineer, Learn (Core Systems) is responsible for redesigning existing components to support enterprise-scale workloads, analyzing and resolving bottlenecks in storage, query performance, APIs, and data models, leading migrations away from legacy implementations to sustainable replacements, improving reliability and efficiency of APIs and integrations for internal and external clients, driving technical projects from definition to delivery with Product Managers and other teams, maintaining a long-term view of system health and architecture, and sharing technical knowledge, reviewing designs, and setting best practices for backend and systems design. The Senior Backend Engineer, Search is responsible for architecting and scaling search infrastructure to billions of documents in multi-tenant environments, designing hybrid search combining keyword search with semantic understanding and vector search, building ranking and personalization systems that learn from user behavior, collaborating with AI engineers to integrate large language models into the search pipeline and build retrieval augmented systems, optimizing search performance across query parsing, index design, and distributed architecture, leading development of search observability and quality frameworks with clear metrics and monitoring, and working closely with product and design to shape the future of knowledge discovery at Sana.

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As a Senior Technical Trainer / Developer Educator for Ema's Agentic AI Builder, responsibilities include owning and evolving the role-based enablement curriculum for developers, admins/ops, and security/compliance roles; delivering instructor-led training, workshops, and office hours; creating progressive learning paths covering fundamentals, workflows, integrations, observability, release lifecycle, and production readiness; building and maintaining hands-on labs, sample projects, reference implementations, and capstone exercises; maintaining starter templates and integration examples; ensuring labs work across customer environments with minimal friction; defining skill milestones and certification criteria; building assessments; partnering with Customer Success to track certification progress; reinforcing training concepts by coaching customers in real implementations; helping customers adopt best practices in agent design, tool reliability, prompt/policy guardrails, testing, evaluation, and release strategies; identifying customer pitfalls for training improvements; collaborating with SRE/DevOps to create production enablement content such as runbooks, SOPs, monitoring checklists, incident simulations; teaching customers to run the AI builder at enterprise scale covering failure handling, secrets management, performance, governance; acting as a feedback loop to synthesize training feedback and highlight product and documentation gaps; and partnering with engineering to keep enablement content up to date with product releases.

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Co-build with customers: Understand discovery calls, translate messy requirements into clear specs, prototype quickly, and iterate to adoption. Own automations end-to-end: Design, build, and maintain low-code workflows using n8n and Clay (webhooks, schedulers, error handling). Customize CRMs: Configure and extend HubSpot/Salesforce for clients (objects, properties/fields, automations, APIs). Build AI agents: Help design and wire up agents using Baserow + n8n (data models, prompts, evaluation loops). Be product-minded: Propose improvements, simplify flows, and turn one-off builds into repeatable templates.