AI Backend Engineer Jobs

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The Backend Software Engineer on the Engine Team (Voice Agent) at Deepgram will lead the design and implementation of the Voice Agent product, including designing and implementing secure, robust, and scalable services for speech processing. Responsibilities include building integrations to support telephony providers, RAG systems, and diverse deployment scenarios; engineering services for testability and observability within a complex chain of AI models; improving core inference services in networking, speech processing, model orchestration, and observability; developing integrations with in-house, third-party, and open-source AI models for perception and conversational dynamics; debugging complex system issues involving networking, scheduling, and highly concurrent workloads; rapidly customizing backend services to support customer needs; and partnering with Product to design and implement new services, features, and products end to end.

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The Software Engineer will own backend services that power core features across both Hiya mobile applications, including caller ID lookups, spam/fraud detection, call screening, real-time scam alerts, call transcription, and AI-generated summaries. Responsibilities include designing service architecture, writing production code, deploying to AWS, and operating systems at scale with high availability and low latency. The role also involves building and maintaining mobile integrations connecting backend services to iOS and Android apps, collaborating with mobile engineers to ensure seamless SDK behavior, debugging platform-specific issues, and shipping reliable features for millions of users. Additionally, the engineer will scale infrastructure to support growing user bases and new feature development, make architectural and implementation decisions balancing speed, quality, and long-term maintainability, solve ambiguous problems in call screening logic and backend structure, and collaborate across engineering, product, and design teams in a high-autonomy environment to ship impactful features.

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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.

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Design, implement, and evolve core APIs that enable agents to take meaningful actions across Zapier's integration ecosystem. Build systems that support scalable orchestration, robust logging, and high-availability execution. Influence and collaborate on the core performance for MCP focusing on memory, context windows, token usage, and reasoning steps. Integrate with Zapier's existing execution infrastructure and help build the interfaces between LLM clients and Zapier. Partner closely with Product, Design, Platform, and Sales teams to ensure the protocol serves both external and internal needs. Drive design decisions that prioritize scalability, performance, security, and clarity for developers and AI systems alike.

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Design, build, and own the core backend systems, including services, data models, and business logic powering Clarion’s AI agents and healthcare workflows. Build infrastructure for multi-step, asynchronous workflows with conditional logic, retries, and failure handling. Own integrations with EHRs and legacy healthcare platforms via APIs and RPA. Create internal abstractions and tooling for deploying and customizing AI assistants for new customers. Implement and maintain backend authentication, authorization, and HIPAA-compliant architecture for enterprise healthcare.

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Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.

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Design and deliver production-grade AI agents for healthcare that handle sensitive patient and member interactions while maintaining strict HIPAA compliance. Drive the Agent Development Life Cycle with ownership from pilot through deployment and iteration. Partner with healthcare leaders to understand challenges and build AI agents that address operational needs. Develop expertise in healthcare systems, workflows, and data/privacy standards to create trustworthy AI experiences. Guide and contribute to the evolution of Sierra's core platform based on customer feedback. Examples of projects include building AI agents for insurance networks, providers, primary and urgent care clinics, and healthcare financial platforms, as well as experimenting with voice models for secure interactions.

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Build and operate backend systems that serve AI-powered features in production; design inference pipelines, orchestration layers, and service boundaries around models; own production concerns including monitoring, logging, alerting, and incident response; optimize latency and throughput across inference, caching, batching, and streaming.

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Build and operate backend systems that serve AI-powered features in production. Design inference pipelines, orchestration layers, and service boundaries around models. Own production concerns including monitoring, logging, alerting, and incident response. Optimize latency and throughput across inference, caching, batching, and streaming.

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Own customer outcomes end to end by working directly with customers, design partners, and internal stakeholders to define technical scope, success criteria, and delivery milestones, then building and shipping the solution. Design and implement features across the full stack with a focus on solving real problems observed in production environments. Integrate deeply with customer environments by working hands-on with cloud platforms, observability systems, CI/CD pipelines, and incident response workflows to ensure product fit. Diagnose and resolve complex issues across customer deployments, turning support interactions into product insights and durable fixes. Build evaluations and feedback loops to quantify customer value and ensure new capabilities are genuinely impactful. Write clean, maintainable, well-tested code, lead design discussions and code reviews, and help shape the technical direction of the product and the engineering culture of the team.