AI Backend Engineer Jobs

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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 Backend Engineer, AI, you own the inference and orchestration layer that powers every AI interaction in the product. You build and operate backend systems that serve AI-powered features in production, design inference pipelines, orchestration layers, and service boundaries around models. You are responsible for production concerns such as monitoring, logging, alerting, and incident response. Additionally, you optimize latency and throughput across inference, caching, batching, and streaming. Your work enables backend systems to run reliably at scale, handling production AI traffic with low latency and high throughput, ensuring APIs are stable, clear, and support seamless integration with frontend and ML systems. You ensure production incidents are quickly detected, diagnosed, and resolved, minimizing user impact, and continuously improve system performance and reliability through iterative changes based on real usage.

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Collaborate with the AI Tools squad to implement and improve AI features in the Photoroom app, including Logo maker, AI Images, AI Videos, and other features on the app homepage. Design and architect new features by chaining a mix of internal and external services to generate images and videos for users. Monitor and scale the growing load on the FastAPI service using Datadog to find optimizations and bottlenecks or implement smart caching of pipeline steps.

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Design and build extensible backend systems that support flexible configurations for different customers and content types. Develop infrastructure that interfaces cleanly with large language models (LLMs), enabling prompt engineering, context injection, and modular evaluation workflows. Build tooling and platforms that enable fast iteration by AI engineers and analysts, including declarative pipelines, parameterized jobs, and reproducible experiments. Prioritize ease of deployment, integration, and testing for both internal teams and external partners. Collaborate closely with product, data, and policy teams to translate nuanced safety needs into scalable, maintainable software systems.

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Design, build, and own backend systems end-to-end, including services, APIs, data pipelines, and infrastructure that power the products. Solve complex technical challenges across distributed systems, scaling, concurrency, and performance. Integrate and operate large generative AI models in production, including deploying, serving, and scaling systems that combine internal research and external capabilities. Instrument, experiment, and iterate in production to continuously improve system and product quality. Design and operate core platform infrastructure, including integrations with third-party providers, storage systems, security, and internal APIs.

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As a Software Engineer, Core Science, you will help design and build the platforms, products, and infrastructure powering AI-native scientific research workflows inside Codex. You will own systems end-to-end from architecture and implementation to evaluation, launch, and production operations, focusing on quality and velocity. Responsibilities include building backend services, data and orchestration pipelines, model-powered workflows, and user-facing experiences. You will shape AI-powered scientific research by building backend systems and full-stack product experiences across Prism and Codex. Develop AI-native workflows for paper writing, literature review, paper understanding, research synthesis, and scientific knowledge exploration. Partner closely with researchers and domain experts across biology and adjacent scientific disciplines to understand research workflows, identify AI opportunities, and translate scientific needs into product capabilities. Build AI-powered tools for scientific data analysis and simulation to help researchers explore data, run experiments, interpret results, and iterate on models or hypotheses. Assist in establishing product, platform, and engineering foundations for fast-moving 0 to 1 efforts, balancing rapid experimentation with rigor and reliability required for research tools.

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Design, build, and maintain scalable backend services and APIs that power Chattermill’s core analytics platform. Architect reliable, maintainable distributed systems and contribute to the evolution of backend service design and infrastructure. Own end-to-end delivery of backend engineering workstreams, from technical scoping and architecture through to implementation, testing, observability, and production support. Integrate language models, agentic frameworks, and AI pipelines into core product and backend services. Drive performance, reliability, and observability across high-throughput distributed data systems, including logging, tracing, alerting, and incident response. Work with cloud infrastructure and distributed systems in GCP (preferred) or AWS environments. Collaborate closely with Product to define scope, shape technical solutions, and explore new platform capabilities and features. Contribute to engineering excellence through code reviews, architectural discussions, and continuous improvement of development standards across the team.

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Own the complete development lifecycle for spam and scam detection infrastructure including research, proposing solutions, implementation, testing, deployment, production maintenance, and monitoring. Participate in on-call rotation for rapid recognition and resolution of production issues while improving system reliability. Design and build frameworks that enable data scientists to develop, test, and deploy complex scam detection models with access to call data in a privacy-aware and regulation-compliant manner. Make independent implementation decisions while driving collaborative design discussions to improve system quality, maintainability, and cost-effectiveness. Evaluate critical tradeoffs between immediate fixes and durable solutions prioritizing service quality and system resilience. Collaborate with product managers, data scientists, and engineering teams to align technical decisions with business impact and user needs. Recognize and promote engineering patterns, design principles, and architectural decisions across teams to raise quality and execution speed. Influence team operations by pushing back on non-aligned solutions, surfacing issues early in project planning, and reasoning about business impact versus cost.

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As a Senior Software Engineer on the Platform - Cloud Events team, you will ensure that data captured by Hayden's devices is properly validated and assessed by ML models that run in the Cloud. You will improve ML operations by enabling a more efficient model improvement lifecycle, work closely with the Product Team, Technical Program Managers, and partner engineering teams to build edge software and interpret sensor data. Responsibilities include maintaining high code quality through code review and documentation, optimizing machine learning operations with robust systems and cross-team collaboration, expanding the event pipeline capabilities to meet general and client-specific needs, and reducing business costs by optimizing the usage of cloud resources, especially GPUs.

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Diseñar, construir y operar servicios core de la plataforma que soportan los flujos de AI/LLM. Desarrollar y correr sistemas basados en LLM, como text-to-SQL, reasoning agents y planners, enfocándose en confiabilidad, performance y costos. Construir y mantener servicios backend y APIs que orquestan pipelines complejos y multi-step. Trabajar hands-on con infraestructura basada en Kubernetes, incluyendo despliegues, escalado y debugging en producción. Contribuir a workflows de DevOps y MLOps, incluyendo CI/CD, ambientes, monitoreo, logging y buenas prácticas operativas. Participar en decisiones de arquitectura relacionadas con observabilidad, manejo de fallas, evaluación y trade-offs del sistema. Colaborar directamente con los fundadores y el equipo de producto para transformar problemas reales de negocio en soluciones técnicas robustas.