СШАHead of Process & Controls - Finance and Operations
1 мес. назад
260k–270k USD / годСШАHeadHybrid
process architecturegovernancefinancial reportingautomation
Lead and build operational processes, controls, and governance for scaling AI infrastructure business.
О компании
- 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, powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, 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:
- 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.
- AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
Обязанности
- 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.
Требования
- 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.
Будет плюсом
- 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.
Другое
- 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:
- 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.
- 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.
- 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
- 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