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About This Role
Requisition ID # 174897
Job Category: Accounting / Finance
Job Level: Individual Contributor
Business Unit: Finance
Work Type: Hybrid
Job Location: Oakland
Department Overview
The Operations Finance team provides critical financial support to PG&E’s core operating and enterprise functions, including financial analysis, budgeting, planning, forecasting, accounting support, operational performance reporting, strategic decision support, and value delivery. Operations Finance serves as a trusted advisor to business leaders by translating operational priorities into clear financial plans, identifying risks and opportunities, and helping the company deliver safe, reliable, affordable, innovative service to customers.
This role will support the Information Technology organization with a dedicated focus on artificial intelligence initiatives, including generative AI, agentic AI, AI-enabled automation, model consumption, cloud AI services, and emerging AI capabilities. The role will help PG&E apply financial rigor to AI investments by connecting technical usage patterns to measurable business value, ensuring savings are validated and captured, and building repeatable standards for AI business cases, cost governance, and value realization. Position Summary
The Expert / Principal Operations Finance Analyst – AI Value Delivery & FinOps will serve as the finance lead for AI initiatives within PG&E’s IT portfolio. This is not a project manager role. The successful candidate will be a strategic finance partner who understands both the AI landscape and the business case discipline required to convert AI activity into tangible financial outcomes.
The role will evaluate AI opportunities across the development lifecycle, determine whether value is expense reduction, capital deferral, productivity, risk reduction, customer experience improvement, or other measurable benefit, and ensure that benefits are modeled, validated, budget-owner-owned, and ultimately taken to the bottom line where applicable. The individual will partner closely with IT, AI product teams, architecture, cloud/FinOps, accounting, sourcing, business finance, and functional area leaders to build repeatable frameworks for AI investment decisions, model usage optimization, tokenomics, O&M tail forecasting, and capital-versus-expense treatment of AI-enabled products and agents.
This position is hybrid, working from the employee’s remote office and our Oakland headquarters, based on business need. Job Responsibilities AI Value Delivery and Business Case Ownership Lead finance support for AI business cases, ensuring costs, benefits, assumptions, risks, timing, dependencies, confidence levels, and value levers are clearly documented and decision ready. Translate AI use cases into financial outcomes, including O&M reduction, capital deferral, avoided work, productivity gains, quality improvements, risk mitigation, customer experience benefits, and other enterprise value levers. Partner with business owners to define how savings will be realized, who owns the benefit, when the benefit will hit forecast/budget, and what actions are required to convert productivity into bottom-line savings. Develop ROI, NPV, payback, sensitivity, and scenario analyses for AI initiatives across pilot, scale, and run-state phases. Establish benefit tracking routines that compare approved business case value to actual realized financial and operational outcomes. AI FinOps, Tokenomics, and Usage Optimization Build and maintain financial models for AI consumption, including token usage, model selection, inference cost, GPU or cloud AI service cost, prompt/context patterns, embeddings, vector database usage, orchestration costs, platform fees, licensing, support, and ongoing run costs. Partner with IT, cloud, enterprise architecture, and AI engineering teams to improve the cost effectiveness of AI workloads through model right-sizing, routing, caching, batching, prompt efficiency, capacity planning, vendor pricing evaluation, and usage guardrails. Create showback, chargeback, or allocation approaches that connect AI consumption to business owners, use cases, products, and outcomes. Analyze pricing sheets, vendor proposals, consumption trends, forecast variances, and unit economics to recommend financially optimal AI model and platform choices. Develop AI cost KPIs such as cost per task, cost per workflow, cost per case, cost per user, cost per avoided hour, cost per token, and cost per business outcome. Accounting, Governance, and Standards Partner with Accounting, Controllers, IT, and project teams to evaluate capital-versus-expense treatment for AI-enabled products, agents, platforms, data work, implementation costs, software development, licensing, and ongoing support. Create repeatable standards, templates, decision trees, and governance routines for AI financial review, including intake requirements, cost categories, benefit taxonomy, accounting considerations, O&M tail assumptions, and value realization evidence. Ensure AI business cases are aligned with PG&E financial policies, utility accounting fundamentals, cost model requirements, regulatory considerations, and internal governance expectations. Support financial controls, auditability, data quality, cost attribution, and documentation needed for defensible AI investment decisions. Identify and resolve gaps in AI cost transparency, ownership, tagging, allocation, and benefit accountability. Planning, Forecasting, and Business Partnership Develop multi-year forecasts for AI initiatives, including pilot costs, scale-up costs, recurring O&M, licensing, cloud consumption, support resources, model retraining, monitoring, governance, and vendor services. Recommend optimized O&M tail assumptions based on actual and expected AI model usage patterns, adoption curves, unit cost trends, vendor pricing, and operational support needs. Support annual planning, monthly forecasting, variance analysis, and leadership reporting for AI-related IT spend and value delivery. Prepare executive-ready financial insights, decision papers, dashboards, and narratives that translate technical AI concepts into business implications for Finance and IT leadership. Challenge assumptions constructively, identify financial risks and opportunities, and influence business partners toward financially sound decisions that maximize ROI. Qualifications Minimum Bachelor’s degree in finance, Accounting, Economics, Business, Information Systems, Data Analytics, or related discipline, or equivalent work experience. 6 years of job-related experience in FP&A, business finance, technology finance, investment analysis, business case development, value delivery, FinOps, or related field. Demonstrated ability to build financial models, evaluate investment tradeoffs, develop business cases, and communicate financial recommendations to business leaders. Experience working with cross-functional stakeholders across Finance, Accounting, IT, product, engineering, sourcing, or operations. Strong understanding of planning, forecasting, budgeting, variance analysis, and financial governance. Desired MBA, CPA, CFA, FinOps Certified Practitioner, cloud financial management certification, or equivalent experience. Experience supporting IT, cloud, AI, data, analytics, software, digital transformation, or enterprise technology portfolios. Working knowledge of generative AI, agentic AI, large language models, model usage patterns, token-based pricing, AI platform economics, and cloud AI services. Experience developing KPI frameworks, value realization models, benefit tracking routines, and executive dashboards. Understanding of utility accounting, regulatory finance, cost model concepts, capital-versus-expense treatment, and software capitalization principles. Ability to simplify complex technical concepts into clear financial narratives, influence decisions without direct authority, and operate in ambiguous, fast-evolving environments. Key Skills and Capabilities AI value delivery and benefits realization AI FinOps, tokenomics, model usage economics, and consumption forecasting Business case development, ROI analysis, NPV, payback, and sensitivity modeling Technology finance, cloud cost management, and vendor pricing analysis Capital-versus-expense analysis and partnership with Accounting Financial planning, forecasting, budget management, and variance analysis Executive communication, stakeholder management, and cross-functional influence Process standardization, governance, financial controls, and continuous improvement What Success Looks Like AI initiatives have clear, decision-ready business cases that connect technical scope, model usage, implementation cost, run-rate O&M, accounting treatment, and measurable business outcomes. Savings and productivity benefits are not left as theoretical value; they are assigned to accountable business owners, incorporated into forecasts or budgets where appropriate, and tracked through realization. AI consumption is financially transparent, with repeatable views of token usage, model selection, unit economics, cost drivers, vendor pricing, and optimized run-state recommendations. Accounting, Finance, IT, and business teams have a common framework for evaluating capital-versus-expense treatment, O&M tails, and financial governance for AI agents, platforms, and enabled capabilities. Leadership can clearly see which AI investments are creating value, which require course correction, and which should be scaled, paused, redesigned, or retired based on financial and operational evidence. PG&E has repeatable AI finance standards that improve investment quality, reduce ambiguity, and create confidence that AI spend is being converted into durable enterprise value.
Compensation
PG&E provides the salary range that the company in good faith believes it might pay for this position at the time of the job posting. This compensation range is specific to the locality of the job. The actual salary paid to an individual will be based on multiple factors, including, but not limited to, specific skills, education, licenses or certifications, experience, market value, geographic location, and internal equity. Although we estimate the successful candidate hired into this role will be placed between $134,000 - $150,000, the decision will be made on a case-by-case basis related to these factors. This job is also eligible to participate in PG&E’s discretionary incentive compensation programs.
Bay Area Minimum: $122,000 Bay Area Maximum: $194,000
Salary Context
This $122K-$194K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Pacific Gas and Electric, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($158K) sits 25% below the category median. Disclosed range: $122K to $194K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Pacific Gas and Electric AI Hiring
Pacific Gas and Electric has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Oakland, CA, US. Compensation range: $194K - $265K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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