AI Backend Engineer Jobs

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Build and scale retrieval infrastructure across indexing, serving, and query execution. Develop low-latency, high-throughput systems for real-time model interaction. Partner with research to productionize embedding and retrieval techniques. Support dense, sparse, and hybrid retrieval pipelines. Own system performance, reliability, and observability at scale. Collaborate across Pretraining, Inference, and Product teams to integrate retrieval end-to-end. Contribute to model-system interfaces for agentic workflows.

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Design and build extensible backend systems supporting flexible configurations for different customers and content types. Develop infrastructure interfacing with LLMs to enable prompt engineering, context injection, and modular evaluation workflows. Build tooling and platforms for fast iteration by AI engineers and analysts, including declarative pipelines, parameterized jobs, and reproducible experiments. Prioritize ease of deployment, integration, and testing for 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 and deliver production-grade AI agents that are highly performant, reliable, and intuitive, running in production environments across industries like finance, healthcare, and commerce. Own and drive the entire Agent Development Life Cycle (ADLC) from initial pilot through deployment and continuous iteration, building, tuning, and evolving AI agents in production environments and defining best practices for ADLC. Partner directly with leaders at large enterprises and cutting-edge startups to understand their business challenges and build AI agents that transform their operations at scale. Collaborate with customers to guide the evolution of Sierra's core platform by surfacing unmet needs, prototyping new tools and features, and working with research, product, and platform teams to shape the future of AI agent development and Sierra's product.

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As a Software Engineer on the AI Voice Agent team, you will work on real-time speech pipeline systems including live audio buffering, streaming, latency optimization, and integrating with speech providers. You will build and improve conversation intelligence systems that manage the LLM layer for natural conversation flow, including prompt construction, context management, function calling, and dialogue management. You will develop the action framework that allows the AI Voice Agent to execute tasks during calls such as querying account data, creating tickets, and checking order status, handling API configuration, success/failure branching, authentication management, and runtime execution. Additionally, you will work on knowledge ingestion, storage, and retrieval for the voice agent and manage memory for retaining information across conversations to improve responses. You will collaborate with designers to create easy-to-use interfaces for agent lifecycle management including creation, configuration, testing, and deployment. You will contribute to building evaluation frameworks and metrics for voice AI quality, post-call analytics, and instrumentation, as well as participate in the on-call rotation.

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As a Software Engineer on the AI Voice Agent team, you will work on real-time systems involving live audio such as buffering, streaming, and latency optimization, along with integrating speech providers. You will build and improve conversation intelligence systems, including prompt construction, context management, function calling, and dialogue management to make conversations feel natural. You will develop the action framework to execute configurable API calls, manage success/failure branching, authentication, and runtime execution during calls. You will work on knowledge ingestion, storage, retrieval, memory, and context for the voice agent to improve its performance over time. Additionally, you will collaborate on agent lifecycle tasks such as creation, configuration, testing, and deployment of voice agents and help build evaluation frameworks for model performance, call quality metrics, and call analytics. Participation in on-call rotations is also expected.

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As a Senior Software Engineer on the AI Voice Agent team, you will work on real-time systems involving live audio streaming and latency optimization integrated with speech providers. You will build and improve conversation intelligence systems that manage LLM layers, including prompt construction, context management, function calling, and dialogue management to create natural, actionable phone conversations. You will develop the action framework allowing configurable API calls with branching logic and runtime execution, supporting tasks like data lookup and ticket creation during calls. You'll manage knowledge ingestion, storage, and retrieval to enhance agent memory and learning over time. You will collaborate with designers to enable customers to create, configure, test, and deploy voice agents through intuitive product experiences. Additionally, you will help develop evaluation frameworks, analytics, call quality metrics, and monitoring instrumentation, and participate in on-call rotation duties.

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Lead and scale Horizon3.ai's Offensive Engineering organization, overseeing teams responsible for exploit development, offensive content, and attack automation within the NodeZero platform. Set clear technical and product direction for how NodeZero identifies, exploits, and validates vulnerabilities across large, complex environments. Partner with Product, Precision Defense, and Platform teams to define and deliver offensive capabilities that influence the roadmap and enhance customer outcomes. Drive execution from proof-of-concept through production to transform cutting-edge attack research into scalable, productized features. Stay hands-on to guide architectural decisions and evaluate exploit and automation approaches, mentoring technical leads in building resilient, modular systems. Build, mentor, and scale diverse teams of software engineers, exploit developers, and offensive researchers, fostering a culture of collaboration, creativity, and engineering excellence that bridges offensive and product software development. Collaborate across engineering, product, and GTM teams to align offensive innovation with business priorities and ensure delivery of impactful capabilities for customers. This role is central to the mission of delivering continuous, autonomous security testing at scale.

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The Full Stack Engineer is responsible for building and maintaining backend services in Python, contributing to APIs and microservices with clean and maintainable abstractions, working with datasets and asynchronous processing pipelines, improving performance, reliability, and observability, participating in system design discussions and learning from senior engineers, collaborating closely with frontend and AI/ML engineers, writing production-quality, testable, and secure code, and participating in code reviews to continuously improve.

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You will design, build, and evolve the public API product for Photoroom, which is the core interface through which developers access the company's AI capabilities. You will ship features used by both self-serve developers and large enterprise customers, ensuring the API remains reliable, scalable, and easy to integrate. You will own the API surface end-to-end, from design decisions such as naming, versioning, and structure to implementation, performance, and long-term maintainability. You will iterate quickly based on real usage, shipping multiple times per week, using data and user feedback to prioritize what matters most. You will work at the intersection of backend engineering and AI, abstracting complex image models into simple, elegant developer-facing interfaces. Collaboration with product, machine learning, backend, and sales teams is required to ensure the API delivers real value to users. You will join a small, senior team with high ownership and direct product impact from day one.

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Build and ship backend features for Taktile's AI agent platform in Python (FastAPI) deployed on AWS serverless infrastructure. Own work end-to-end by collaborating on scope, implementation, testing, releasing, and iterating based on real usage and customer feedback. Improve how agents run, connect to external tools, and behave when errors occur. Use AI tools daily to move faster, improve quality, and build AI-native capabilities. Review pull requests in depth, improve test coverage and CI/CD, and raise engineering excellence and reliability. Engage in team rituals including daily syncs, planning sessions, demos, and technical deep-dives. Grow professionally by learning from experienced team members, contributing to foundational product layers, and participating in cross-team learning groups. Participate in daytime operations duty and join an on-call rotation to support system ownership and DevSecOps skill growth.