APIs AI Jobs

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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.

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Lead customer discovery and design sessions to map business processes, identify automation opportunities, and define solution architecture. Design, build, and deploy integrations using low/no-code platforms (Zapier, Make, n8n, Workato) and CRM automation tools (HubSpot Workflows, Salesforce Flow) with API connectors. Collaborate with Engineering to validate technical feasibility, resolve blockers, and share field learnings that inform product improvements. Configure and optimize the AI Agent by defining intents, prompts, actions, guardrails, and performance metrics. Manage complex, cross-functional deployments by defining timelines, aligning stakeholders, ensuring accountability, and delivering on time and within scope. Create scalable models and reusable frameworks such as templates, playbooks, and reference architectures to expedite future projects. Champion continuous learning and enablement through training peers, running internal workshops, and documenting best practices to raise the technical bar across the team. Run global, targeted outbound campaigns within the existing customer base to generate pipeline and accelerate adoption, working closely with the customer marketing team. Collaborate with GTM leadership to embed routines and cadences that drive accountability for new product pipeline, forecast accuracy, and performance tracking. Own regional top-line targets for assigned products by collaborating with AEs and AMs who hold add-on quotas. Act as an internal product owner within the GTM function by defining product-specific MRR strategies, coordinating cross-functional support, and ensuring delivery of the AI-enabled communication platform. Collaborate with Product and PMM to shape the AI Voice Agent roadmap based on customer needs, integration insights, and field learnings. Drive internal and external product education including enablement for System Integrators and channel partners. Maintain deep awareness of AI and CX industry trends to keep Aircall's positioning competitive and feed insights back into product and GTM strategies.

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As an Atlas Engineer at Basis, you will own internal systems and agents end-to-end, from scoping to deployment. Responsibilities include identifying opportunities, designing automations, building internal agents for coding, operations, and knowledge management, defining and maintaining context architectures, extending and evaluating agents with prompts, tools, and safety layers, and creating feedback loops by instrumenting impact, analyzing trajectories, and iterating behavior. You will automate high-leverage workflows by identifying manual processes for automation, building and extending internal tools such as Retool apps, custom scripts, or APIs, integrating SaaS systems and internal data sources, and optimizing when to code versus configure. Additionally, you will build connective systems across teams by partnering with engineering, operations, and accounting, mapping bottlenecks, designing systems to remove them, building lightweight internal products like CLI tools, Slack bots, and Chrome extensions, and documenting your work for extensibility. You will operate as the Responsible Party for your projects, planning, building, iterating, shipping fast, instrumenting impact, measuring improvement, running postmortems, refining processes, and sharing learnings and reusable frameworks.

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Build and maintain components of the AI agent platform, internal tools, and backend systems. Develop and test APIs and integrations with external platforms like ServiceTitan. Participate in the full product cycle from idea through prototype to production. Improve agent intelligence and voice performance in real time. Collaborate with senior engineers to deliver high-impact features and maintain code quality.

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As a Backend Platform Engineer at Harvey, you will build and operate the backend platform that supports all company services, designing and implementing shared frameworks and libraries to abstract common concerns and improve developer experience. Responsibilities include developing and maintaining internal backend frameworks and libraries for capabilities such as API routing, service lifecycle management, caching, messaging, and error handling, creating and improving APIs, service templates, and versioned interfaces, and championing modern backend architecture patterns like asyncio and streaming data processing. You will design Harvey-specific abstractions and domain-specific frameworks covering cross-cutting concerns and areas like data governance and event processing. Embedding reliability and observability through tracing, metrics, and automated tests to ensure robustness and ease of monitoring is required. Collaboration with the Model Infrastructure team, Developer Experience and Infrastructure teams to integrate platform components, handle GenAI-native application challenges, and gather feedback from product engineering teams is part of the role. Evangelizing best practices and providing strong defaults and clear documentation to facilitate fast and confident development by product teams is also expected.

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As a Forward Deployed Engineer, you will partner directly with customers to understand their biggest challenges and rapidly design, implement, and integrate technical solutions that drive real impact. You will leverage engineering expertise to design scalable AI workflows for clinical and operational use cases, producing professional-grade systems and data-flow diagrams defining interfaces, failure modes, and integration points with EHRs and legacy systems. You will build and deploy Large Language Model (LLM) powered workflows, applying advanced prompting strategies, building agentic workflows, and tuning vector stores for quality, latency, and cost. You will perform technical validation of AI algorithm outputs and ensure strict adherence to HIPAA/SOC 2 controls, establishing guardrails such as audit trails and least-privilege access for safe clinical deployment. You will independently manage technical implementation plans, defining scope and setting expectations with customers, driving prototypes into production and facilitating the transition to steady-state ownership alongside Customer Success teams. You will serve as the technical voice in customer meetings, translating complex platform capabilities into clear value propositions for executives and non-technical stakeholders. Additionally, you will provide input to internal cross-functional teams on platform gaps to help widen competitive advantage.

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You will design and build backend and infrastructure systems for OpenAI’s monetization and ads stack, emphasizing reliability, privacy, security, and large-scale performance. You’ll develop APIs and platforms, drive 0→1 infrastructure projects, and collaborate cross-functionally with Product, Research, and Design teams.