About the Job
Founded in Australia in 2019, our mission is to create the most efficient AI infrastructure by combining cutting-edge technology with a steadfast commitment to sustainability.
At Firmus, we are unique in our approach. We design, build, and operate a new class of digital infrastructure – the AI Factory. Through our model-to-grid technology approach, we have pushed the boundaries of multi-generational liquid cooling systems, energy management, AI software orchestration, and construction. Fo...
Our large-scale GPU cloud platform, Firmus AI Cloud, is purpose-built to deliver energy-efficient AI compute at scale to customers.
Key Responsibilities
Own end-to-end UX design and front-end experience quality for the AI Cloud Portal,
Global Operations Console, Bare Metal as a Service, Kubernetes as a Service, and future
XaaS products.
Conduct user discovery and workflow analysis with key personas, including AI
researchers, ML engineers, application developers, cloud administrators, tenant
administrators, infrastructure operators, support engineers, data-centre teams, and
external customers.
Translate product requirements, user needs, operational workflows, technical
constraints, and system telemetry into user journeys, information architectures,
wireframes, prototypes, interaction patterns, and production-ready interfaces.
Design self-service experiences for discovering, requesting, provisioning, configuring,
accessing, monitoring, scaling, updating, and decommissioning bare-metal GPU capacity
and Kubernetes environments.
Design clear workflows for GPU and AI workload management, including job submission,
job templates, model and workload recipes, queue selection, quota visibility, priority
requests, capacity reservations, workload status, failures, retries, and support
escalation.
Create usable interface patterns that explain proprietary scheduler decisions, including
why a job is queued, admitted, deferred, placed on a particular topology, pre-empted,
rescheduled, or unable to run.
Design Model-to-Grid experiences that help users and operators understand the
relationship between model or workload requirements, benchmark results, GPU
resources, network topology, storage, capacity, power, thermal conditions, and
scheduling outcomes.
Create user-facing workflows for selecting and applying validated model, training, fine-tuning, and inference recipes based on workload goals such as latency, throughput,
accuracy, cost, energy efficiency, GPU availability, or time-to-results.
Design intuitive benchmarking experiences, including benchmark configuration,
execution tracking, result exploration, comparison of baselines and variants,
reproducibility metadata, bottleneck identification, and actionable optimization
recommendations.
Shape the Global Operations Console experience for monitoring and managing fleet-level AI-factory operations, including cluster health, capacity, workload demand,
infrastructure events, service status, scheduler health, resource utilization, maintenance
activities, and operational incidents.
Make complex infrastructure status and telemetry actionable through dashboards, drill-down views, alerts, guided troubleshooting, root-cause context, recommended actions,
and role-appropriate escalation paths.
Design agentic application experiences that clearly distinguish between agent
observations, recommendations, planned actions, approved actions, completed actions,
failures, and human intervention points.
Create safe and trustworthy human-in-the-loop workflows for agentic operations,
including approval queues, permission scopes, action previews, confidence or evidence
views, audit trails, rollback options, and incident escalation.
Design user experiences for AI platform identity, access, tenancy, quotas, approvals,
notifications, billing or consumption visibility, support, and administrative controls in
partnership with Security and platform teams.
Develop and maintain a scalable design system, component library, interaction
standards, accessibility guidelines, content patterns, and visual language suitable for
cloud, AI-platform, and operations-console products.
Implement high-quality front-end experiences or work closely with front-end engineers
to ensure designs are technically feasible, performant, responsive, accessible, and
consistently implemented.
Partner with the Product Manager / TPM / Release Manager to define user-facing
product requirements, UX acceptance criteria, phased release plans, usability validation,
release notes, product documentation, and adoption measures.
Work with AI, DevOps, Platform, SDI, Security, and operations teams to ensure
interfaces accurately represent system state, operational constraints, permissions, data
freshness, risk level, and action consequences.
Define and analyze UX metrics, customer feedback, product analytics, workflow
completion, feature adoption, support trends, task duration, error rates, and usability
findings; use them to prioritize iterative improvements.
Champion a product-led, user-centered approach across technical teams by making user
workflows, usability risks, and customer outcomes visible early in product definition and
delivery.
Required Skills & Abilities
5+ years of experience in UX engineering, product design, interaction design, front-end
engineering, or a comparable role delivering enterprise, developer, cloud, or
operational software products.
A portfolio demonstrating end-to-end design and delivery of complex web applications,
platforms, control planes, developer tools, cloud consoles, data-intensive products, or
operational dashboards.
Strong ability to convert complex technical systems and workflows into intuitive user
journeys, information architecture, interaction models, and production-quality
interfaces.
Practical experience designing for multiple user personas, permission levels, account or
tenant boundaries, high-stakes operations, self-service workflows, and complex
configuration tasks.
Proficiency in modern product-design tools such as Figma or comparable tools for
wireframing, prototyping, design systems, user flows, and developer handoff.
Strong front-end engineering capability in modern web technologies, such as TypeScript,
JavaScript, React, Next.js, Vue, Angular, or equivalent frameworks.
Experience building or contributing to component libraries, design systems, responsive
web applications, data visualization patterns, and accessible user interfaces.
Understanding of user research, usability testing, user interviews, workflow mapping,
prototype validation, product analytics, A/B testing where appropriate, and iterative
design methods.
Familiarity with cloud infrastructure, Kubernetes, bare-metal provisioning, distributed
systems, observability, job scheduling, developer platforms, or infrastructure
operations.
Technical curiosity and the ability to understand AI/ML platform concepts, including
GPU resources, distributed workloads, training, inference, model serving,
benchmarking, workload recipes, queues, quotas, and performance metrics.
Ability to interpret metrics and telemetry relevant to AI-platform interfaces, including
GPU utilization, queue time, job state, workload status, cluster health, latency,
throughput, capacity, error rates, cost, and energy-related indicators.
Familiarity with AI application patterns, including RAG, agentic workflows, model
recommendations, human approval flows, AI-generated insights, confidence and
provenance indicators, and conversational or copiloted interfaces.
Experience designing security-conscious user experiences involving authentication,
RBAC, tenant boundaries, approval workflows, secrets or credentials, auditability,
privacy, and controlled actions.
Strong written communication skills, including ability to produce concise UX rationale,
interaction specifications, content guidance, workflow documentation, and releaseready design artefacts.
