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About This Role
Company Overview
Plus Power is an energy storage market leader, with a market\-leading 10\+ GW portfolio across more than 25 states that will transform North American electric grids into cleaner and more versatile critical infrastructure.
Standalone energy storage is rapidly transforming the North American energy markets, because it is cheaper than new natural gas plants, faster to build than fossil peakers or transmission, and able to perform diverse energy services. Plus Power partners with electricity system operators, utilities, and investors to originate, develop, finance, own and operate standalone energy storage projects that provide critical services to the wholesale electric market. Plus Power's team applies an intentional mindset to energy storage development by using a data\-driven approach to development and operations.
At Plus Power, we are focused on solving hard climate problems, profitably. We are growing fast, and value candidates who, like us, share a focus on setting high expectations, owning and learning from mistakes in the spirit of radical transparency, and are committed to internal partnering as a key element of our ideas meritocracy. Our team praises Plus Power's culture and excels through our game\-changing mission and supportive ecosystem.
About the Role
Plus Power recruits outstanding energy industry professionals who are driven to develop, build and operate assets safely and reliably to decarbonize the power markets while growing their careers. Our team looks for data\-driven and fact\-based mindsets, engaging and collaborative behaviors, and personal growth\-focused professionals.
The Manager, AI Solutions \& Analytics on the Business Operations \& Strategic Analytics (BO\&SA) team will work under the guidance of the Senior Director, Business Operations \& Strategic Analytics. The Manager will focus on operationalizing AI adoption across Plus Power by enabling teams to use AI effectively and responsibly, supporting cross\-functional experimentation, and scaling repeatable, high\-value workflows into durable ways of working. Initially, the role will be more heavily focused on enablement, adoption support, and establishing the AI operating model across the business. Over time, the role will increasingly focus on facilitating the transition from experimentation to scaled implementation by helping design, operationalize, and embed AI solutions into business processes, workflows, and platforms.
This role serves as the enablement, implementation, and scaling layer across the business, helping translate business needs into practical AI\-enabled workflows, reinforcing standards and guardrails, and integrating AI into our existing data and analytics stack. The role partners closely with cross\-functional stakeholders to support prioritization, adoption, governance, and value realization, while ensuring AI initiatives are aligned to business needs and embedded into existing processes. The ideal candidate combines strong business judgment with fluency in AI, data, analytics, automation, and change management. They can move comfortably between enablement, experimentation, and scale, influence stakeholders without formal authority, and help teams adopt new tools and ways of working with confidence and consistency. This role is deeply embedded in BO\&SA's broader analytics, reporting, and operational intelligence initiatives and will work closely with leaders and teams across the organization.
Key Responsibilities
- Drive implementation of Plus Power's AI operating model by building organizational capability, supporting adoption, and enabling responsible use of AI across the business.
- Build organizational capability for AI through training, guidance, reusable patterns, and practical support that helps teams apply AI effectively in day\-to\-day work.
- Define and reinforce AI standards, guardrails, and usage patterns to promote consistency, quality, trust, and responsible adoption across teams.
- Partner with leaders and cross\-functional stakeholders to assess, prioritize, and implement AI opportunities with meaningful business impact and strong operational fit.
- Design, develop, and support AI\-enabled solutions and workflows, including copilots, agents, prompt\-based workflows, and human\-in\-the\-loop processes, with attention to practicality, governance, and execution quality.
- Facilitate the transition from experimentation to scale by identifying high\-value use cases, standardizing proven approaches, and embedding AI solutions into durable business processes and platforms.
- Contribute to BO\&SA's broader analytics, reporting, and operational intelligence priorities, while helping measure AI adoption, solution effectiveness, and business impact over time.
Skills \& Qualifications
- Minimum BA/BS in economics, data science, computer science, business, or related field.
- 6\-10 years of experience in analytics, business intelligence, automation, technology or data\-focused role.
- Ability to synthesize and present complex data in a clear and actionable manner.
- Ability to effectively manage multiple priorities and deliver timely, high\-quality results.
- Highly organized with impeccable attention to detail.
- Experience building agentic AI solutions in a business environment, including designing and deploying agents, orchestrating multi\-step workflows, integrating enterprise data and systems, and implementing appropriate human\-in\-the\-loop controls.
- Experience driving adoption of new technologies in a cross\-functional business environment.
- Experience translating ambiguous business problems into practical workflows, decision frameworks, or scaled processes.
- Experience establishing governance, standards, and guardrails for analytics, automation, or AI\-enabled workflows.
- Ability to assess and communicate business value, tradeoffs, and scale potential of AI use cases.
- Strong change management and stakeholder engagement skills.
- Excellent communication and interpersonal skills and ability to work collaboratively in a cross\-functional environment.
- Advanced experience with enterprise AI platforms including Microsoft Copilot Studio, Perplexity AI and Claude.
- Advanced experience with modern data and analytics platforms including Microsoft Fabric, Azure Databricks, AWS, and Snowflake.
- Advanced experience with SQL, Python, or other data analysis tools.
- Advanced proficiency in the Microsoft suite of tools and applications including Outlook, Word, PowerPoint, and Excel.
- Prior experience in energy is preferred.
Compensation, Location, and Benefits
Highly competitive total compensation from one of North America's leading energy storage developers, owners and operators. Flexible, work from home or hybrid work from Plus Power's offices in San Francisco, Houston, Chicago, Seattle, New York, Palm Beach and Birmingham.
The expected salary range\* for this position begins at $120,000\. We may ultimately pay more or less than the posted range based on several factors including, but not limited to relevant experience, skills, qualifications, geographic labor market, and other factors consistent with applicable law. This position is also eligible to participate in our annual bonus program.
Plus Power offers a competitive and comprehensive benefits program, unlimited vacation, flexible remote work, work from home stipend, educational assistance, parental leave, and a highly engaging company culture with opportunities for in\-person connection and learning and growth.
The deadline\* for applying to this role is 9/4/2026
*Plus Power is committed to a diverse and inclusive workplace where people of all backgrounds can thrive. Plus Power is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.*
- *This information is provided in accordance with applicable law.*
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Plus Power, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Plus Power AI Hiring
Plus Power has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in The Woodlands, TX, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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