Product Manager

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.

Report directly to the Head of AI & Applications to translate strategic priorities into a prioritized product portfolio, quarterly roadmap, release plan, and sprint-level delivery plan.
Establish and maintain a single source of truth for product scope, roadmap priorities, feature status, release commitments, dependencies, risks, decisions, and delivery health across the AI & Applications team.
Lead product-definition activities for new capabilities by developing problem statements, user and customer value propositions, feature briefs, product requirements, acceptance criteria, non-functional requirements, and success measures.
Ensure every roadmap initiative has a clear product narrative explaining the intended user outcome and the technical capability being delivered, including its relationship to models, data, benchmarking, optimization, infrastructure, scheduling, inference or agentic applications.
Break strategic initiatives into sequenced epics, features, stories, technical milestones, validation activities, and production-release increments that can be delivered through sprint-based execution.
Own roadmap refinement and sprint-readiness processes, ensuring work entering a sprint has sufficient definition, an accountable owner, realistic estimates, known dependencies, acceptance criteria, and agreed delivery priorities.
Coordinate planning across AI engineers, application engineers, inference and optimization engineers, DevOps and scheduler engineers, Platform, Infrastructure, Cybersecurity, and other stakeholders.
Identify, document, manage, and drive resolution of dependencies between teams, systems, vendors, environments, hardware availability, datasets, model availability, security reviews, infrastructure readiness, and release approvals.
Maintain an integrated release plan across all relevant workstreams, including feature development, platform changes, benchmark execution, model or recipe validation, security controls, observability, documentation, support readiness, and communications.
Define and operate release gates for development completion, code review, automated and manual testing, benchmark validation, performance regression testing, security review, operational readiness, documentation, rollback planning, and production approval.
Run a regular product-delivery operating cadence, including backlog refinement, sprint planning, sprint reviews, roadmap reviews, release-readiness reviews, dependency forums, risk reviews, and leadership updates.
Track sprint progress and release health against agreed commitments; identify schedule, scope, quality, capacity, and dependency risks early; and propose recovery actions, sequencing changes, or trade-off decisions.
Provide concise and decision-oriented reporting to the Head of AI & Applications, including roadmap progress, sprint delivery, release forecast, key milestones, dependencies, blockers, risks, decisions needed, and recommended next actions.
Ensure planned releases are validated against the intended outcomes, including product functionality, developer or user experience, benchmark performance, operational reliability, security posture, and adoption metrics.
Lead post-release reviews to confirm delivered value, identify defects or operational gaps, capture lessons learned, and feed prioritized improvements into the subsequent sprint and roadmap cycle.
Coordinate the technical product roadmap across key capability areas, including:
Model-to-grid benchmark libraries, finger print engine, simulation, performance measurement, and optimization workflows.
Kubernetes-based workload orchestration, custom job-scheduler features, queues, quotas, fairness, placement, multi-tenancy, and GPU utilization.
Training, fine-tuning, and inference recipes, including model configurations, distributed execution, quantization, compilation, batching, parallelism, and performance validation.
Inference services, serving APIs, throughput and latency optimization, cost efficiency, reliability, observability, and operational readiness.
Agentic applications, RAG, telemetry integration, diagnosis and recommendation workflows, tool use, approval flows, and safe operational automation.

5+ years of experience in product management, technical program management, delivery management, release management, solutions architecture, engineering management, or a comparable technical leadership role.
Proven experience working directly with an engineering or product leader to establish delivery discipline, roadmap transparency, release predictability, and cross-functional accountability.
Demonstrated ownership of complex, multi-team technical product roadmaps with interdependent software, platform, infrastructure, operational, and security workstreams.
Strong experience with agile product delivery, including backlog management, epic decomposition, sprint planning, story refinement, estimation, sprint reviews, retrospectives, velocity tracking, and release planning.
Practical experience building and managing integrated delivery plans, dependency maps, release calendars, RAID logs, decision logs, and executive-level status reporting.
Strong technical understanding of AI/ML platforms and applications, including training, fine-tuning, inference, model serving, benchmarking, performance optimization, data pipelines, RAG, and agentic workflows.
Familiarity with GPU-accelerated infrastructure, Kubernetes, Slurm or comparable workload schedulers, distributed training, multi-tenant resource management, and AI inference systems.
Understanding of product and engineering success measures such as feature adoption, user satisfaction, release quality, defect rates, throughput, latency, GPU utilization, benchmark reproducibility, cost efficiency, and operational reliability.
Experience creating product requirements, technical specifications, acceptance criteria, release checklists, validation plans, release notes, and customer or internal documentation.
Strong analytical ability, including the capacity to turn incomplete information, technical constraints, delivery data, and stakeholder feedback into practical prioritization and execution decisions.
Excellent written, verbal, and stakeholder-management skills, including the ability to communicate technical progress, risks, dependencies, and decision options clearly to senior leadership.