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About This Role
About Longroad Energy
Longroad Energy is a Boston, MA headquartered renewable energy developer focused on the development, ownership, and operation/asset management of wind and solar energy projects throughout North America. Founded in 2016, Longroad owns 4\.5 GW of wind and solar projects across the United States in addition to operating and managing a total of 6\.2 GW of wind and solar projects on behalf of Longroad and third parties. Our vision is to create lasting value for our shareholders, communities, and employees by responsibly developing, owning, and operating renewable energy projects. We have assembled a world\-class team with a passion for renewable energy innovation and a commitment to developing renewable projects throughout the US.
Position Summary
Longroad is hiring its first Director of AI Strategy \& Transformation to define how AI will reshape the way Longroad operates — from project finance and asset management to legal, regional development, and operations — and to lead the change required to get there. This is a newly created role within the IT organization, chartered directly by the senior leadership team.
The role begins as a strategic, assessment\-oriented one. In the first phase, the Director will work across every function to understand Longroad’s current state, define a multi\-year enterprise AI strategy, and lay the foundation — tooling, governance, operating model, and change management — for sustainable transformation. The Director is expected to roll up their sleeves where it matters most, but will spend the majority of their time on strategy, advisory, and transformation leadership. As the program scales, this role is expected to grow, with the potential to add an AI Analyst direct report to extend hands\-on delivery capacity.
The successful candidate is part strategist, part change agent, and part technologist. Equally credible challenging the senior leadership team on how AI changes the business and working alongside a team to advance a specific capability, they will define the multi\-year vision, partner with functional leaders, and serve as Longroad’s lead AI evangelist. This role will be onsite and based in San Francisco, CA or Boston, MA.
Responsibilities:
- Enterprise Strategy \& Assessment. Lead an enterprise\-wide assessment of Longroad’s current AI usage, capability gaps, governance processes, and high\-value transformation opportunities. Develop and steward Longroad’s multi\-year AI strategy, including platform standards, build\-vs\-buy decisions, operating\-model implications, and a prioritized investment roadmap aligned to company strategy across commercial, operating, and back\-office priorities.
- AI\-Driven Business Transformation. Partner with the senior leadership team to identify where AI fundamentally changes how Longroad works — what gets done, by whom, how, and at what cost — and translate that into concrete workflow redesigns and capability shifts. Push the organization beyond incremental tool use to durable changes in operating model and talent density.
- Change Management \& Cultural Adoption. Serve as Longroad’s senior change agent for AI transformation in partnership with Human Resources. Define and execute a change management plan that brings employees along, builds AI fluency, and addresses the cultural, organizational, and workflow change required to realize value, anticipating and removing barriers along the way.
- Evangelism \& Executive Advisory. Serve as Longroad’s lead AI evangelist and trusted advisor to the senior leadership team and department leaders. Translate a rapidly evolving AI market into pragmatic, Longroad\-specific recommendations that drive business value. Challenge leaders constructively where AI changes the right answer.
- Tooling, Standards \& Governance. Own the enterprise AI tool stack in coordination with the broader IT organization and develop working fluency across Longroad's broader technology portfolio — data infrastructure, operational systems, and SCADA — to ensure AI strategy is built on and aligned with the company's full technology landscape. Standardize on a primary platform (Claude is the current direction), rationalize redundant tooling, and establish guardrails for data handling, vendor due diligence, model selection, and acceptable use — aligned with Longroad’s information security, data, and infrastructure standards. Evaluate and govern agentic AI deployments, including multi\-step workflows, tool\-use patterns, and human\-in\-the\-loop design — with particular attention to operational and SCADA\-adjacent contexts where reliability and auditability requirements are high.
- Cross\-Functional Use Case Delivery. Partner with leaders across project finance, investments, asset management, operations, legal, corporate development, regional development, planning, HR, and finance to identify and deliver high\-impact use cases. Provide hands\-on coaching and demonstration support where it accelerates value, while building the surrounding teams’ capacity to deliver on their own.
- Capability Building. Establish the foundation for Longroad’s AI enablement program — including role\-based learning paths, an internal community of practice, and an AI ambassadors’ network — so transformation is owned across the business rather than dependent on a single individual.
- Measurement \& Reporting. Define how Longroad measures AI transformation and value — emphasizing business outcomes (cycle\-time, decision quality, capacity unlocked) over surface usage metrics. Report to the VP of IT and senior leadership on a regular cadence and adjust the strategy as the technology and the business evolve.
Required Qualifications:
- 10\+ years of professional experience, with significant time leading enterprise strategy, business transformation, or large\-scale change programs.
- Demonstrated track record as a transformation leader — proven ability to drive durable change in how a business operates, build executive alignment, and sustain behavior change across functions.
- Broader technology leadership experience or exposure across enterprise data infrastructure, operational technology, or IT governance, with interest in growing into a broader technology leadership mandate over time.
- Hands\-on fluency with leading frontier LLM platforms (Claude strongly preferred), including practical experience with prompt design, agents and skills, and integration patterns.
- Strong strategic thinking and executive presence — equally credible challenging the CEO and senior leaders and working alongside a team to advance a specific use case.
- Excellent written and verbal communication, with a teaching and coaching instinct.
- Comfort operating as a department of one in the early phase, with the judgment to know when to build, buy, or partner.
Preferred Qualifications:
- Experience leading technology\-based transformation engagements, or in an internal transformation role at a comparable enterprise
- Prosci Change Management Certification or PMP Certification
- AWS Machine Learning Certification
- Experience in the renewable energy industry (development, project finance, asset management, or operations).
- Familiarity with the document and data ecosystems common to our industry: PPAs, tax equity term sheets, interconnection queues, GIS/KMZ data, permit filings, and ISO/RTO datasets.
- Exposure to AI governance topics: data privacy, model evaluation, third\-party risk, and responsible\-use frameworks.
- Experience designing and leading enablement programs such as ambassador networks, communities of practice, or internal certifications.
Salary/Compensation
The salary range is between $185k\-225k. In addition to base salary, this role includes a bonus. The position offers health, vision/dental insurance, flexible spending accounts, 401(k) plan, accrued paid time off, life insurance and disability coverage.
We encourage candidates to consider total compensation when applying and if you feel you meet the requirements and are excited to learn more, we’d love to share.
- *Note that the* *pay* *range listed for this position is a good faith and reasonable estimate of the range of possible base compensation at the time of posting.* *Exact compensation may vary based on skills, experience and location.*
Other
Applicants must be currently authorized to work in the United States. The Company does not sponsor applicants for work visas.
Benefits of Working at Longroad Energy We are dedicated to providing our employees with the support and resources they need to stay healthy, secure their future, and be successful in their careers. Benefits at Longroad include the opportunity for merit\-based salary increases, incentive plan participation, eligibility for our 401 (k) plan and matching, and comprehensive medical, dental, vision, life, and disability insurance. Our robust time\-off policy includes accrual of 18 vacation days in your first year, paid holidays, and paid volunteer time. We offer paid parental leave to help support employees as they transition into parenthood. Learn more about our employee benefits.
Diversity, Equity \& Inclusion Diversity, equity, and inclusion matter \- at Longroad, in our industry, in our communities, and in society at large. We embrace our responsibility to build and promote a diverse, equitable, and inclusive working experience and drive change where we live and operate. We work to actively promote and celebrate diversity, equity, and inclusion. We foster a supportive space that empowers everyone at Longroad to learn about, discuss and ask questions related to embracing and honoring identity. We collaborate with our community, colleagues, and industry in the ongoing pursuit of evolving and growing an inclusive and diverse environment. Learn more about our DEI commitment.
Longroad Energy Values At Longroad, we SHINE. We aim to be the most trusted renewable energy company on the long road to a green future. We are an experienced team of problem solvers and promise\-keepers who develop sustainable solutions that meet today’s challenges and make a lasting impact on people and our planet. Learn more about our SHINE values.
Longroad Energy is proud to be an Equal Opportunity Employer (“EOE”). Qualified applicants are considered for employment without regard to age, race, color, religion, sex, national origin, disability, veteran status, citizenship, or any other legally protected status. Longroad Energy prohibits discrimination against individuals with disabilities and will reasonably accommodate applicants with a disability, upon request, and will also ensure reasonable accommodations are made for disabled employees. Longroad Energy is firmly committed to ensuring equal employment opportunities in all employment practices and personnel actions, including advertising, recruitment, testing, screening, hiring, selection for training, upgrading, transfer, demotion, layoff, discipline, termination, rates of pay, and other forms of compensation.
Salary Context
This $185K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Longroad Energy, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($205K) sits 13% above the category median. Disclosed range: $185K to $225K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Longroad Energy AI Hiring
Longroad Energy has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $225K - $225K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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