Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI. We are 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.
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:
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:
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:
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:
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:
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:
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.
The Head of Process & Controls will transform the operating environment in three stages: 1. Build the foundation Establish clear processes, ownership, controls, authoritative data, operating standards, and system accountability. 2. Clean up and stabilize Eliminate fragmented workflows, recurring discrepancies, unnecessary manual activities, inconsistent data, and control gaps. 3. Automate and scale Deploy automation and AI so routine activities, reconciliations, validations, and exception detection occur with minimal manual intervention.
Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
