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About This Role
Job Family:
Data Science Consulting Travel Required:
Up to 10% Clearance Required:
NoneWhat You Will Do:
We are seeking an experienced Associate Director to join our growing AI and Data practice, with a dedicated focus on Evaluation, Measurement \& Verification (EM\&V) of utility energy efficiency and demand side management (DSM) programs for commercial energy providers. This individual will be a hands\-on leader, responsible for overseeing evaluations for complex portfolios of DSM programs and driving business growth through proactive sales efforts. The Associate Director will oversee cross\-functional teams and collaborate directly with client executives and business leaders to drive value through cutting\-edge evaluation of program including of load management and demand response, energy efficiency, renewable, EV, energy storage, decarbonization, and electrification. This is a leadership role for someone who thrives at the intersection of EM\&V, AI and advanced analytics and market growth.
Client Leadership \& Engagement
- Lead client engagements end\-to\-end from strategy through implementation, which can include supporting program design, defining evaluation methodologies, overseeing execution of the evaluation, presenting results including the translation of findings into meaningful and actionable recommendations, and developing regulatory filings.
- Develop and document evaluation methodologies using best practice, leading\-edge techniques.
- Oversee and collaborate with team members and take ownership of project schedules, budgets, and delivery excellence.
- Drive new business opportunities by identifying client needs, shaping proposals, and expanding relationships into strategic partnerships.
- Develop and maintain relationships with key clients and stakeholders to build grow accounts and influence buying decisions.
Solution Development \& Innovation
- Drive end\-to\-end solution development leveraging AI and advanced analytics, with a strong emphasis on utility customer program evaluation methodologies.
- Stay ahead of industry trends and emerging technologies to inform solution development and position offerings competitively in the market.
Team \& Practice Leadership
- Mentor and lead multi\-disciplinary consulting teams including data scientists, engineers, and business consultants.
- Own business development activities including pipeline generation, proposal development, and strategic pursuits to meet growth targets.
- Contribute to recruiting, talent development, and thought leadership within the practice.
What You Will Need:
- Minimum SEVEN (7\) years of experience in the energy industry with a proven track record in managing projects and business development—including client relationship management, opportunity identification, and contribution to revenue growth.
- Minimum FIVE (5\) years of experience leading DSM program evaluations.
- Deep understanding of DSM customer program evaluation methodologies for load management and demand response, energy efficiency, renewable, EV, energy storage, decarbonization, and electrification programs.
- Ability to translate technical results into actionable insights and value propositions during client engagements.
- Demonstrated proficiency in one or several programming languages (such as R and R’s “Tidyverse” packages, Python, SQL), version control (such as GitHub), and AI\-assisted coding (such as GitHub Copilot).
- Demonstrated proficiency with data visualization and experience with data visualization tools (e.g., R Shiny, Dash, PowerBI).
- Demonstrated proficiency with spreadsheets, databases, word processing, and slide presentation software.
- Academic or professional experience with one or several AI software, such as CoPilot, ChatGPT, Gemini, Claude
- Ability to assume ownership of significant portions of tasks while collaborating with a close\-knit team.
- Proven experience across the business development lifecycle—including opportunity identification, capture strategy, and proposal development—while ensuring alignment with technical delivery.
- Track record of leading large\-scale evaluation engagements from concept through execution, while simultaneously expanding client relationships and uncovering new revenue opportunities.
- Skilled at motivating and guiding multi\-disciplinary teams of evaluation specialists to deliver at scale while fostering a growth\-oriented culture.
- Exceptional communication, facilitation, and relationship\-building skills that drive trust, collaboration, and commercial success.
What Would Be Nice To Have:
- Experience with AI/ML technologies, modern data platforms (e.g., Snowflake, Databricks, AWS/GCP/Azure), and advanced analytics methodologies
- AI/LLM Certifications
- Project Management Professional (PMP)
\#LI\-DNI
The annual salary range for this position is $135,000\.00\-$225,000\.00\. Compensation decisions depend on a wide range of factors, including but not limited to skill sets, experience and training, security clearances, licensure and certifications, and other business and organizational needs. What We Offer:
Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace.
Benefits include:
- Medical, Rx, Dental \& Vision Insurance
- Personal and Family Sick Time \& Company Paid Holidays
- Position may be eligible for a discretionary variable incentive bonus
- Parental Leave and Adoption Assistance
- 401(k) Retirement Plan
- Basic Life \& Supplemental Life
- Health Savings Account, Dental/Vision \& Dependent Care Flexible Spending Accounts
- Short\-Term \& Long\-Term Disability
- Student Loan PayDown
- Tuition Reimbursement, Personal Development \& Learning Opportunities
- Skills Development \& Certifications
- Employee Referral Program
- Corporate Sponsored Events \& Community Outreach
- Emergency Back\-Up Childcare Program
- Mobility Stipend
About Guidehouse
Guidehouse is an Equal Opportunity Employer–Protected Veterans, Individuals with Disabilities or any other basis protected by law, ordinance, or regulation.
Guidehouse will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable law or ordinance including the Fair Chance Ordinance of Los Angeles and San Francisco.
If you have visited our website for information about employment opportunities, or to apply for a position, and you require an accommodation, please contact Guidehouse Recruiting at 1\-571\-633\-1711 or via email at [email protected]. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodation.
All communication regarding recruitment for a Guidehouse position will be sent from Guidehouse email domains including @guidehouse.com or [email protected]. Correspondence received by an applicant from any other domain should be considered unauthorized and will not be honored by Guidehouse. Note that Guidehouse will never charge a fee or require a money transfer at any stage of the recruitment process and does not collect fees from educational institutions for participation in a recruitment event. Never provide your banking information to a third party purporting to need that information to proceed in the hiring process.
If any person or organization demands money related to a job opportunity with Guidehouse, please report the matter to Guidehouse’s Ethics Hotline. If you want to check the validity of correspondence you have received, please contact [email protected]. Guidehouse is not responsible for losses incurred (monetary or otherwise) from an applicant’s dealings with unauthorized third parties.
*Guidehouse does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Guidehouse and Guidehouse will not be obligated to pay a placement fee.*
Salary Context
This $135K-$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 Guidehouse Inc., 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. Disclosed range: $135K 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.
Guidehouse Inc. AI Hiring
Guidehouse Inc. has 4 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, MLOps Engineer. Positions span Arlington, VA, US, New York, NY, US, Washington, DC, US. Compensation range: $188K - $225K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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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