AI Backend Engineer Jobs

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Build simulation environments where AI agents can be trained and evaluated on realistic SRE work including debugging, incident response, infrastructure changes, and on-call grind. Work directly with Research Scientists and domain experts to translate real SRE scenarios into reproducible, graded environments. Own the architecture and implementation of large subsystems from design through long-term evolution, including infrastructure, tooling, data pipelines, and scoring. Design, build, and scale backend services and APIs that generate, run, and score scenarios in high-concurrency environments. Ensure reliability, scalability, and performance across the system, especially in distributed components. Ship initial versions quickly, observe performance, identify issues, and refine. Focus on creating environments that accurately reflect real production failures. Use AI tools throughout the development stack for code generation, scenario generation, and debugging to increase productivity.

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Build and scale core components of Reinforcement Learning Environments (RLE) and infrastructure, design and implement backend systems and data pipelines, translate ambiguous product and research needs into production systems, develop modular, reusable workflow domains, and improve reliability, observability, and system performance.

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Lead and contribute to cross-functional efforts solving complex physical design challenges across IPs, projects, and advanced technology nodes. Develop and enhance RTL-to-GDS methodologies, including floorplanning, synthesis, P&R, STA, signoff, and assembly. Architect and deploy AI/ML-driven solutions in production flows to improve engineering efficiency, turnaround time, and QoR. Optimize EDA tools and custom CAD flows using data-driven and ML-based techniques, in close collaboration with verification, extraction, timing, DFT, and EDA vendors.

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As a Product Backend Engineer, you will design and operate backend systems that enable AI capabilities to provide dependable product experiences. Responsibilities include collaborating closely with Product to prioritize customer-focused work and deliver reliable features quickly, designing and owning backend services and APIs for web applications, workflows, and integrations, modeling and managing data in Postgres and related data stores, building secure, multi-tenant, permissions-aware systems with appropriate auditing, implementing backend features using LLMs and agentic tools, collaborating with frontend, product, and design teams to define API contracts and ship features end-to-end, adding logging, metrics, and tracing for service observability and on-call readiness, improving performance and scalability by profiling and tuning, and participating in code reviews, technical design discussions, and an on-call rotation for the services you own.

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As a Product Backend Engineer, design and operate backend systems that enable AI capabilities to deliver seamless and dependable product experiences. Build secure, multi-tenant services, orchestrate interactions with large language models (LLMs) and agentic tools, and define backend architecture for expanding product offerings. Collaborate with Product to prioritize customer-focused work and deliver reliable features quickly. Design and own backend services and APIs supporting web applications, workflows, and integrations. Model and manage data in Postgres and related data stores to ensure low-latency and reliable user experiences. Build permissions-aware systems with appropriate auditing for enterprise and government customers. Implement backend features that interact with AI systems via robust, well-structured services. Collaborate with frontend, product, and design partners for solution design, API contract definition, and end-to-end feature delivery. Add logging, metrics, and tracing for service observability and on-call readiness. Improve performance and scalability by profiling, tuning, and refining service boundaries. Participate in code reviews, technical design discussions, and an on-call rotation for owned services.

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Act as a whole-systems thinker by making meaningful system design decisions and owning the architecture of core platform components from initial design through production deployment. Develop secure generative AI services and applications using Python and modern frameworks to drive enterprise-wide transformation. Build and optimize high-performance and low-latency APIs and microservices for integrating advanced AI models and agentic workflows into the enterprise platform. Drive proactivity without red tape by clearly communicating changes, plans, and proposals to cross-functional teams without waiting for approval. Bridge the gap between backend services, infrastructure, and operations to ensure seamless deployments and scalable architecture.

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The Staff Software Engineer is responsible for architecting, designing, and developing the intelligence layer that controls GPU node assignment, monetization, and management within Crusoe's fleet. They will be among the first engineers on the Virtual Pool Service and Capacity Management Intelligence systems, shaping implementation and making key design decisions, building foundational cloud platform infrastructure. Responsibilities include building the Virtual Pool Service as the source of truth for GPU node states and history, designing the Capacity Management Intelligence automation layer to handle allocation and automated node lifecycle transitions, collaborating cross-functionally to architect and implement physical infrastructure management systems, championing system reliability, scalability, and security, streamlining cloud deployment and operations using technologies such as Go, gRPC, NATS event streaming, PostgreSQL on Kubernetes, and Netbox, and mentoring engineers while contributing to team growth in collaboration with engineering management.

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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.