About Us: 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. Key Responsibilities Process Foundation, Standardization & Transformation 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 • 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. AI Automation & Manual Effort Reduction 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 • 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. Workflow & Process Governance 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. Token & Usage Accuracy 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 • Customer-specific infrastructure consumption Establish automated reconciliations between infrastructure telemetry, metering platforms, pricing systems, customer contracts, billing systems, supplier data, and the general ledger. De