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# Staff Backend Engineer, AI Systems

Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move.

[Apply for this job](#job-application)[View company](https://jobicy.com/company/mural.md)Share26 Aug 2026Published24Listing views2Application actions25 Sep 2026Apply before  Opportunity details

## About this role.

AI SummaryMural is hiring a Staff Backend Engineer to build the foundational systems behind its agentic AI collaboration platform. The role focuses on distributed backend architecture for agent orchestration, durable execution, memory, tool integrations, observability, and offline evaluation. It requires extensive production engineering experience and the ability to translate open-ended AI product needs into scalable, reliable services and APIs. The engineer will set technical direction, mentor colleagues, and contribute to hiring and engineering practices within a remote-first AI Foundations team.

## Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

### Job Complexity

5/5EasyHard

### Pace & Pressure

4/5RelaxedFast-paced

### Autonomy Level

5/5GuidedFull ownership

### Communication Load

4/5IndependentCollaborative

AI insightThis is a staff-level systems role involving ambiguous architecture decisions, production reliability, and emerging agentic-AI patterns. Success requires deep distributed-systems judgment alongside practical LLM, retrieval, evaluation, and cross-functional product expertise.

## Salary analysis

Estimated compensation compared with the broader UK market for similar roles.

Estimated job medianHighly competitive£124,218UK market range£100k–£145k0£160k

AI insightThe disclosed yearly salary range is £110,416–£138,020, with a midpoint of £124,218. This sits within a competitive UK market range of approximately £100,000–£145,000 for a Staff Backend Engineer working on AI platforms and distributed systems; actual compensation may vary by location, scope, and total-reward components.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[Backend Engineering](https://jobicy.com/jobs?search_keywords=Backend%20Engineering.md)[Distributed Systems](https://jobicy.com/jobs?search_keywords=Distributed%20Systems.md)[Agentic AI](https://jobicy.com/jobs?search_keywords=Agentic%20AI.md)[LLM Orchestration](https://jobicy.com/jobs?search_keywords=LLM%20Orchestration.md)[API Design](https://jobicy.com/jobs?search_keywords=API%20Design.md)[Node.js](https://jobicy.com/jobs?search_keywords=Node.js.md)[MongoDB](https://jobicy.com/jobs?search_keywords=MongoDB.md)[Azure OpenAI](https://jobicy.com/jobs?search_keywords=Azure%20OpenAI.md)[Observability](https://jobicy.com/jobs?search_keywords=Observability.md)[System Architecture](https://jobicy.com/jobs?search_keywords=System%20Architecture.md)

Sample interview questionsHow would you design a durable agent-execution platform for workflows that may run for hours or days?I would separate workflow state from execution workers, persist each state transition and tool result, and use idempotency keys to make retries safe. The system would use a queue or event-driven scheduler, explicit timeout and retry policies, dead-letter handling, and a durable audit trail so workflows can resume after failures or deployments.

What approach would you take to managing short-term and long-term memory for AI agents?

I would define memory tiers based on purpose: recent conversational context, structured product or user context, and longer-term retrieved knowledge. Each tier should have clear retention, access-control, freshness, summarization, and compaction policies, with evaluation measures to verify that retrieval improves outcomes without introducing irrelevant or unsafe context.

How would you evaluate whether an agent is reliable enough for a production feature?

I would combine offline and online evaluation. Offline, I would maintain representative task suites with expected tool-use behavior, quality rubrics, and regression thresholds; online, I would monitor success rates, latency, tool failures, user corrections, escalation rates, and sampled traces, then use feedback to prioritize fixes.

Describe how you would make an AI agent observable and debuggable in production.

I would implement end-to-end tracing that connects user requests, model calls, retrievals, prompts, tool invocations, workflow states, and final outputs. Metrics and structured logs would capture latency, cost, errors, retries, and quality signals, while privacy controls would redact sensitive content and role-based access would protect trace data.

How do you balance technical leadership with hands-on delivery in a staff engineering role?

I align teams around a clear architecture and measurable outcomes, break ambiguous work into sequenced decisions and milestones, and stay close enough to implementation to remove high-risk technical blockers. I also document decisions, mentor engineers through design reviews and pairing, and create standards that allow the team to deliver independently over time.

### ABOUT THE TEAM

The AI Foundations Team at Mural is pioneering how generative AI transforms visual collaboration and decision-making. We’re a remote-first group of engineers, designers, and product thinkers focused on helping teams work together more effectively. Our goal isn’t to replace human creativity. It’s to amplify it, building AI that enhances how people align, communicate, and make decisions visually.

### YOUR MISSION

You will design and build the core AI systems and platforms that enable Mural’s next wave of agentic, AI-driven collaboration experiences.

Rather than building isolated AI features, you’ll work on the core backend systems that power Mural’s agent platform, including agent orchestration, durable execution, contextual memory, tool integration, observability, and evaluation. Your work will enable intelligent agents to reason over product context, act on behalf of users, and operate reliably and safely at scale. Our stack at Mural includes Azure OpenAI, React, Node, MongoDB.

### WHAT YOU’LL DO

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Build the core backend systems that power Mural’s agent platform, including orchestration, durable execution, tool execution, memory, observability, and evaluation infrastructure

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Design scalable services and APIs that allow AI agents to retrieve context, coordinate multi-step workflows, interact with Mural data, and act reliably on behalf of users

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Develop the agent memory layer, including systems for conversation context, product context, retrieval, summarization, compaction, and long-term context management

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Create infrastructure to monitor, debug, and improve agent behavior through traces, metrics, feedback loops, and offline evaluation

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Translate complex, open-ended product needs into clear backend architectures, service boundaries, data models, and implementation plans that align technical capabilities with user value

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Help define the technical direction for agentic AI at Mural, contributing to long-term architecture and strategy

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Champion engineering excellence, mentoring others and establishing best practices for building reliable AI systems

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Contribute to team growth through hiring, process improvements, and fostering a culture of curiosity, collaboration, and inclusivity

### WHAT YOU’LL BRING

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7+ years of software engineering experience, with strong expertise designing, building, and operating reliable production systems, scalable services and APIs, and well-defined system architectures

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Experience designing and operating distributed systems for complex workflows, including orchestration, asynchronous processing, job execution, retries, event-driven architectures, long-running processes, and failure recovery

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Familiarity with AI, LLM, or agentic systems, including concepts such as tool use, context retrieval, evaluation, or model orchestration

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Strong focus on production quality, including observability, reliability, testing, security, maintainability, and incident response

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Strong communication skills, with the ability to produce clear technical documentation and collaborate cross-functionally

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Experience working full stack or collaborating closely on product-facing frontend experiences

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Deeper AI/ML experience, especially with LLM evaluation, experimentation, embeddings, retrieval systems

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Experience with knowledge graphs, graph-based data modeling, entity resolution, or relationship-aware retrieval systems

### OUR STACK

Azure OpenAI, React, Node, MongoDB

### Equal Opportunity

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

Show more

[Apply now >](https://jobicy.com/jobs/151711-staff-backend-engineer-ai-systems.md)

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