Head of Process & Controls - Finance and Operations

Fireworks • San Mateo, CA

2d • $260,000 - $270,000

About The Position

Fireworks is seeking a Head of Process & Controls to build and lead the operational processes, controls, and governance required to scale our AI infrastructure business. This role will own the end-to-end process architecture across AI infrastructure workflows, token and usage accuracy, Order-to-Cash (O2C), and Purchase-to-Pay (P2P), with a strong mandate to build the process foundation, remediate and simplify existing workflows, and drive AI-enabled automation. The leader will take a phased transformation approach: Build the foundation → Standardize → Automate and scale with AI. The objective is to create reliable, well-controlled processes that accurately connect infrastructure consumption to customer billing, supplier costs, accounting, and financial reporting—while progressively reducing manual effort, reconciliation burden, operational discrepancies, and financial leakage. The ideal candidate combines strong process transformation and controls expertise with an understanding of cloud and AI infrastructure economics. They will work across Engineering, Infrastructure, Product, Finance, Accounting, Procurement, Sales Operations, and Data teams to create scalable workflows and embed automation into the operating model.

Requirements

• 10+ years of experience in process transformation, controllership, finance operations, business operations, internal controls, or related disciplines.
• Significant leadership experience building or transforming complex, cross-functional processes.
• Demonstrated experience establishing process foundations in environments with immature, fragmented, or rapidly evolving workflows.
• Strong expertise in Order-to-Cash and Purchase-to-Pay processes.
• Experience designing financial and operational control frameworks in technology, cloud infrastructure, SaaS, hyperscale infrastructure, or other consumption-based businesses.
• Strong understanding of usage-based billing, metering, reconciliation, pricing, and consumption economics.
• Track record of simplifying processes and reducing manual work through automation.
• Experience partnering with Engineering, Data, Product, Finance, Accounting, and Procurement organizations.
• Strong knowledge of ERP, billing, procurement, data, and financial systems.
• Experience designing automated reconciliations and exception-management processes.
• Familiarity with AI-enabled workflow automation, anomaly detection, and intelligent operations.
• Familiarity with SOX, financial reporting controls, audit requirements, and accounting processes.
• Ability to understand complex data flows and translate technical workflows into scalable business and financial processes.

Nice To Haves

• Experience operating in a high-growth AI, cloud infrastructure, hyperscaler, or technology company.
• Experience with consumption-based revenue and cost models.
• Familiarity with cloud providers and AI infrastructure vendors.
• Experience implementing or scaling usage metering, billing, FinOps, procurement, reconciliation, or workflow platforms.
• Experience leading large-scale process clean-up, transformation, and automation programs.

Responsibilities

• Build the foundational process architecture for AI infrastructure and related financial operations.
• Define clear end-to-end processes, ownership, controls, decision rights, system-of-records, data handoffs, approval points, exception paths, and escalation procedures.
• Assess the current operating environment and identify fragmented or undocumented processes, manual workarounds, spreadsheet-dependent workflows, duplicate activities, unclear process ownership, control gaps, inconsistent data definitions, reconciliation issues, system integration gaps, and recurring operational and financial discrepancies.
• Lead the Standardization of existing processes before introducing automation, ensuring that inefficient or poorly controlled processes are not simply automated in their current state.
• Develop standard operating procedures, process maps, RACI frameworks, control matrices, data definitions, and operational playbooks.
• Establish consistent process and control standards across functions, regions, products, customers, and infrastructure providers.
• Create a transformation roadmap that prioritizes foundational process improvements, risk remediation, automation opportunities, and scalable system capabilities.
• Lead the transition from manual and reactive operations toward AI-enabled, automated process execution and controls.
• Identify high-volume, repetitive, reconciliation-intensive, and exception-driven activities that can be automated.
• Partner with Engineering, Data, Finance Systems, and Product teams to implement AI-enabled workflows for areas such as automated usage reconciliation, token and compute anomaly detection, customer billing validation, vendor invoice validation, contract-to-billing checks, purchase-order and invoice matching, revenue and cost leakage identification, accrual estimation, data-quality monitoring, exception classification and routing, root-cause analysis, variance investigation, duplicate or missing transaction detection, automated control evidence collection, and operational reporting and management insights.
• Design processes around exception-based management, where systems and AI handle routine transactions and teams focus primarily on material exceptions, judgment, and remediation.
• Establish measurable targets for reducing manual touchpoints, spreadsheet dependency, reconciliation effort, processing time, error rates, and discrepancies.
• Ensure AI-enabled automation includes appropriate governance, human review, traceability, validation, and controls.
• Standardize the end-to-end process framework for AI infrastructure operations, from infrastructure provisioning and consumption through usage measurement, customer billing, vendor settlement, accounting, and reporting.
• Map and optimize workflows across compute, GPU, model inference, API and token consumption, capacity allocation, and related infrastructure services.
• Establish clear process ownership, approval points, system interfaces, data handoffs, service-level expectations, exception management, and escalation procedures.
• Partner with Engineering and Infrastructure teams to design operational processes that remain scalable as AI workloads, customers, infrastructure providers, and commercial models grow.
• Ensure controls and financial requirements are embedded into infrastructure workflows by design rather than added downstream.
• Standardize framework governing the accuracy, completeness, and traceability of AI token and infrastructure usage data.
• Ensure reliable measurement and reconciliation of metrics such as input and output tokens, API consumption, model inference usage, GPU or accelerator utilization, compute hours, storage and network consumption, reserved and on-demand capacity, and customer-specific infrastructure consumption.
• Establish automated reconciliations between infrastructure telemetry, metering platforms, pricing systems, customer contracts, billing systems, supplier data, and the general ledger.
• Develop preventive and detective controls to identify missing usage, duplicate usage, incorrect token counts, incorrect pricing, unbilled consumption, misallocated consumption, customer/vendor usage mismatches, unexpected consumption patterns, and data pipeline failures.
• Partner with Data and Engineering teams to establish authoritative data sources, common definitions, and end-to-end data lineage from infrastructure activity through financial reporting.
• Drive continuous improvement in token accuracy, usage completeness, billing accuracy, and cost attribution.
• Own and continuously improve the end-to-end Order-to-Cash process, including contract / order → provisioning → usage capture → rating and pricing → invoicing → accounts receivable → collections → revenue reporting.
• Build a standardized and scalable O2C foundation that supports both committed and consumption-based business models.
• Ensure customer contracts, pricing structures, committed capacity, minimum spend agreements, usage-based pricing, credits, and discounts are correctly translated into operational and billing systems.
• Establish automated controls over usage-to-invoice reconciliation and billing completeness.
• Reduce manual billing adjustments and recurring discrepancies through better upstream data quality, system integration, and automated validation.
• Develop processes for billing adjustments, credits, disputes, contract amendments, and usage exceptions.
• Partner with Revenue Accounting to ensure operational processes support accurate and timely revenue recognition.
• Drive improvements in billing accuracy, invoice timeliness, collections, customer experience, and revenue leakage prevention.
• Own and optimize the Purchase-to-Pay process for AI infrastructure and related services, including capacity planning → purchase commitment → purchase order → service consumption → invoice validation → accrual → payment → supplier reconciliation.
• Build a standardized P2P foundation connecting procurement commitments, infrastructure usage, supplier contracts, invoices, and accounting.
• Establish controls ensuring vendor invoices accurately reflect contracted pricing and actual infrastructure consumption.
• Reduce manual invoice review and reconciliation through AI-enabled invoice validation and exception detection.
• Partner with Infrastructure and Procurement teams on commitments for cloud services, GPUs, data centers, networking, model providers, and other infrastructure vendors.
• Develop processes for committed-use agreements, prepaid capacity, minimum commitments, volume discounts, credits, and variable consumption charges.
• Ensure appropriate accruals and cost allocations where supplier invoices lag infrastructure consumption.
• Design and maintain a scalable controls framework across infrastructure operations and financial processes.
• Define preventive and detective controls covering usage completeness and accuracy, token accuracy, pricing accuracy, customer billing, supplier invoicing, revenue completeness, infrastructure cost completeness, contract compliance, approval authorities, reconciliations, data integrity, and manual adjustments.
• Prioritize automated controls over manual controls wherever technically and operationally feasible.
• Establish control owners, evidence requirements, review frequencies, exception thresholds, and remediation processes.
• Create automated exception monitoring so discrepancies can be identified close to the point of occurrence rather than through downstream month-end reconciliation.
• Partner with Accounting, Internal Audit, and external auditors to support financial reporting and audit requirements.
• Where applicable, build processes and controls capable of supporting SOX-compliant operations.
• Drive automation across infrastructure-to-finance workflows and systematically reduce reliance on spreadsheets, manual reconciliations, and human intervention.
• Partner with Product, Engineering, Finance Systems, and Data teams to define requirements for usage metering, rating and pricing, billing, procurement, ERP, reconciliation, data platforms, workflow management, and AI-enabled exception management.
• Develop a scalable architecture connecting infrastructure telemetry to operational, commercial, and financial systems.
• Establish dashboards and automated monitoring for control effectiveness, reconciliation exceptions, billing accuracy, supplier discrepancies, cost allocation, and financial leakage.
• Create a prioritized automation backlog based on transaction volume, manual effort, financial risk, discrepancy rates, and business impact.
• Track realized benefits from automation, including productivity improvements, reduced error rates, faster cycle times, and reduced operational cost.
• Establish KPIs and KRIs measuring both the health of the underlying processes and progress toward automation.
• Lead regular cross-functional operating reviews to identify systemic issues, assign ownership, and drive remediation.
• Use recurring exceptions and discrepancies as inputs into process redesign and automation priorities rather than treating them as isolated operational issues.
• Provide management with transparency into operational risks, financial exposure, automation progress, and opportunities for further simplification.
• Build and lead a high-performing Process & Controls organization as the company scales.
• Act as the connective layer between AI Infrastructure, Engineering, Finance, Accounting, Procurement, Sales Operations, Product, Data, and Internal Audit.
• Establish clear accountability for processes that span multiple functions and systems.
• Influence system architecture and operating-model decisions to ensure financial controls, data integrity, automation, and scalability are designed into processes from the beginning.
• Create a culture of continuous improvement in which the organization moves from manual → standardized → controlled → automated → AI-enabled.
• Balance operational rigor and financial control with the speed required in a rapidly evolving AI environment.

Benefits

• Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.
• Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.
• Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results.
• Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed