Backend Engineer (Core, Learn, & Agents)

Sana

ABOUT SANA Sana is an AI lab building superintelligence for work. We believe organizations can accomplish their missions faster when teams can effortlessly access knowledge, automate repetitive work, and learn anything with the help of agentic AI. As part of Workday, we are committed to building AI that augments people - not replaces them. We bring this mission to life through two products. Sana Agents provide a seamless way to access all your company’s apps, knowledge, and data, enabling AI agents to do real work so teams can process and act on information at unprecedented scale. Sana Learn is an AI-powered learning hub that combines the simplicity of a modern learning platform with intelligent features like an AI tutor, automated content generation, and interactive apps, making knowledge not just accessible but actionable. We’re a talent-dense, product-obsessed team of engineers and designers from companies like Google, Spotify, Apple, and Databricks, united by deep technical excellence and rapid iteration. Our tools already help over a million people learn and work better across hundreds of leading enterprises - and we’re just getting started. ABOUT THE ROLES You will work on the core backend systems that power Sana’s Learn platform and search infrastructure. You will redesign and scale systems to handle enterprise workloads, remove deep bottlenecks, and evolve our architecture for the era of AI and agents. We are hiring for two senior roles: 1. Senior Backend Engineer, Learn (Core Systems) 2. Senior Backend Engineer, Search 1. SENIOR BACKEND ENGINEER, LEARN (CORE SYSTEMS) ABOUT THE ROLE You will focus on making the Learn platform, especially Manage, Insights, and APIs, scalable and maintainable for enterprise use. You will take systems that work at current scale and redesign them to handle much larger volumes of data, users, and entities. The work is primarily architectural and system level, with strong emphasis on Postgres and database design. IN THIS ROLE, YOU WILL * Redesign existing components to support enterprise scale workloads * Analyze and resolve bottlenecks in storage, query performance, APIs, and data models * Lead migrations away from legacy implementations to sustainable replacements * Improve reliability and efficiency of APIs and integrations for internal and external clients * Drive technical projects from definition to delivery with Product Managers and other teams * Maintain a long term view of system health and architecture * Share technical knowledge, review designs, and set best practices for backend and systems design WHAT SUCCESS LOOKS LIKE * Architecture and scalability are prioritized over short term fixes * Risks are identified early and addressed with solid technical solutions * You balance hands on coding with architectural leadership * Learn core systems support larger enterprise customers without performance or reliability issues * Strong Postgres skills (schema design, query optimization, indexing) improve performance and scalability 2. SENIOR BACKEND ENGINEER, SEARCH ABOUT THE ROLE You will build and scale Sana’s search infrastructure for enterprise scale knowledge discovery. You will architect retrieval systems, ranking algorithms, and search APIs that handle large increases in volume while maintaining sub second latency and high relevance. You will rethink the search stack for the era of agents, using engines like Vespa, vector databases, and embedding models. IN THIS ROLE, YOU WILL * Architect and scale search infrastructure to billions of documents in multi tenant environments * Design hybrid search that combines keyword search with semantic understanding and vector search * Build ranking and personalization systems that learn from user behavior * Collaborate with AI engineers to integrate large language models into the search pipeline and build retrieval augmented systems * Optimize search performance across query parsing, index design, and distributed architecture * Lead development of search observability and quality frameworks with clear metrics and monitoring * Work closely with product and design to shape the future of knowledge discovery at Sana WHAT SUCCESS LOOKS LIKE * Search systems scale elegantly to billions of documents and millions of queries with strict latency targets * Systems apply proven IR techniques and practical advances in semantic AI retrieval * Search quality issues are detected early through strong observability and automated regression checks * You balance implementation with system design and mentor others on search and IR fundamentals * Deep expertise in search infrastructure (index optimization, query planning, distributed retrieval, caching) prevents performance issues before users see them * The stack evolves from traditional search to AI powered discovery, including embeddings, reranking, and RAG, while staying reliable * Search infrastructure supports enterprise deployments with strict SLAs, multi tenancy isolation, and very high uptime OUR TECH STACK We build on a simple modern stack optimized for both humans and AI. * Backend: TypeScript, Kotlin, Node.js * Frontend: TypeScript, React, Tailwind * Databases: Postgres, Redis * Cloud infra: GCP/Kubernetes/Terraform WHAT WE OFFER * Help shape AI's future alongside brilliant minds from Google, Apple, Spotify, Notion, Slack, Databricks, and BCG. * Competitive salary complemented with a competitive RSU program. * Swift professional growth in an evolving environment, supported by a culture of continuous feedback and mentorship from senior leaders. * Work with talented teammates across 5+ countries, and global customers, from our beautiful office in Stockholm.

Sana

ABOUT SANA Sana is an AI lab building superintelligence for work. We believe organizations can accomplish their missions faster when teams can effortlessly access knowledge, automate repetitive work, and learn anything with the help of agentic AI. As part of Workday, we are committed to building AI that augments people - not replaces them. We bring this mission to life through two products. Sana Agents provide a seamless way to access all your company’s apps, knowledge, and data, enabling AI agents to do real work so teams can process and act on information at unprecedented scale. Sana Learn is an AI-powered learning hub that combines the simplicity of a modern learning platform with intelligent features like an AI tutor, automated content generation, and interactive apps, making knowledge not just accessible but actionable. We’re a talent-dense, product-obsessed team of engineers and designers from companies like Google, Spotify, Apple, and Databricks, united by deep technical excellence and rapid iteration. Our tools already help over a million people learn and work better across hundreds of leading enterprises - and we’re just getting started. ABOUT THE ROLE You'll build the core agent infrastructure that powers Sana's mission to bring superintelligence to work. This is a greenfield opportunity to define how AI agents plan, reason, and execute across enterprise environments—building systems that reliably handle real-world complexity at scale. You'll work at the intersection of agent architecture, context-, tool- and prompt engineering, and production infrastructure. IN THIS ROLE, YOU WILL * Architect multi-step planning, orchestration, and tool routing for agents * Implement code generation agents and sandboxed code execution * Engineer memory, state, and context packing/grounding strategies * Balance latency, quality, and cost controls for agent execution * Develop safe fallbacks, graceful degradation and robust error handling * Collaborate with platform and search teams to deliver reusable agent infrastructure * Establish safety guarantees and measurable quality improvements WHAT SUCCESS LOOKS LIKE * Agent architectures are robust, reliable, and enterprise-ready * Context and tool engineering enables agents to act intelligently, at scale * Clear metrics, automated evaluation, and continuous improvement in agent capabilities * Quality and safety are measurable and continuously improving * Durable building blocks accelerate product teams and unlock new capabilities OUR TECH STACK We build on a simple modern stack optimized for both humans and AI. * Backend: TypeScript, Kotlin, Node.js * Frontend: TypeScript, React, Tailwind * Databases: Postgres, Redis * Cloud infra: GCP/Kubernetes/Terraform WHAT WE OFFER * Help shape AI's future alongside brilliant minds from Google, Apple, Spotify, Notion, Slack, Databricks, and BCG. * Competitive salary complemented with a competitive RSU program. * Swift professional growth in an evolving environment, supported by a culture of continuous feedback and mentorship from senior leaders. * Work with talented teammates across 5+ countries, and global customers, from our beautiful office in Stockholm.

Workday Sweden Aktiebolag

Sana is an AI lab building superintelligence for work. We believe organizations can accomplish their missions faster when teams can effortlessly access knowledge, automate repetitive work, and learn anything with the help of agentic AI. As part of Workday, we are committed to building AI that augments people - not replaces them. We bring this mission to life through two products. Sana Agents provide a seamless way to access all your company’s apps, knowledge, and data, enabling AI agents to do real work so teams can process and act on information at unprecedented scale. Sana Learn is an AI-powered learning hub that combines the simplicity of a modern learning platform with intelligent features like an AI tutor, automated content generation, and interactive apps, making knowledge not just accessible but actionable. We’re a talent-dense, product-obsessed team of engineers and designers from companies like Google, Spotify, Apple, and Databricks, united by deep technical excellence and rapid iteration. Our tools already help over a million people learn and work better across hundreds of leading enterprises - and we’re just getting started. About the Role You'll build the core agent infrastructure that powers Sana's mission to bring superintelligence to work. This is a greenfield opportunity to define how AI agents plan, reason, and execute across enterprise environments—building systems that reliably handle real-world complexity at scale. You'll work at the intersection of agent architecture, context-, tool- and prompt engineering, and production infrastructure. In this role, you will Architect multi-step planning, orchestration, and tool routing for agents Implement code generation agents and sandboxed code execution Engineer memory, state, and context packing/grounding strategies Balance latency, quality, and cost controls for agent execution Develop safe fallbacks, graceful degradation and robust error handling Collaborate with platform and search teams to deliver reusable agent infrastructure Establish safety guarantees and measurable quality improvements About You Basic Qualifications: 3+ years of software engineering experience building production backend or platform systems. 3+ years of experience in TypeScript, with a strong track record of writing reliable, maintainable services. 3+ years of experience with distributed systems, APIs, asynchronous workflows, and service-oriented architecture. 3+ years of experience designing systems with a focus on scalability, reliability, observability, and maintainability. Other Qualifications: Experience building and deploying LLM-powered applications in production. Experience building agent platforms or AI infrastructure. Deep understanding of the low-level details of the OpenAI, Google, and Anthropic LLM APIs, including tool calling, system prompt caching, etc. Familiarity with LLM application patterns, including tool calling, retrieval-augmented generation (RAG), memory and context management, multi-step orchestration, and human-in-the-loop systems. Experience building and running machine learning systems in production, including compiling training and test datasets, building training pipelines, evaluating models, and detecting and handling drift (neural networks, Gaussian models, Thompson sampling, etc.). Experience designing evaluation frameworks for LLM or agent quality and safety, including hands-on use of platforms such as Langfuse or LangSmith. Familiarity with vector databases, prompt and context engineering, and experimentation tooling. Experience working with sandbox environments such as Modal, and designing strict access control models to keep user data safe and encrypted at all times. Experience running services in Kubernetes-based environments on GCP or equivalent cloud platforms. Comfort working with Postgres and Redis in high-throughput, low-latency service contexts. Contributions to open source TypeScript projects. Ability to navigate ambiguity, make strong technical tradeoffs, and drive projects from concept to production. Strong communication and collaboration skills, with the ability to partner effectively across engineering, product, and AI research teams.