Interested in this AI/ML Engineer role at AES Corporation?
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About This Role
Are you ready to be part of a company that's not just talking about the future, but actively shaping it? Join The AES Corporation (NYSE: AES), a Fortune 500 company that's leading the charge in the global energy revolution. With operations spanning 14 countries, AES is committed to shaping a future through innovation and collaboration. Our dedication to innovation has earned us recognition as one of the Top Ten Best Workplaces for Innovators by Fast Company in 2022\. And with our certification as a Great Place to Work, you can be confident that you're joining a company that values its people just as much as its groundbreaking ideas.
AES is proudly ranked \#1 globally in renewable energy sales to corporations, and with $12\.7B in revenues in 2023, we have the resources and expertise to make a significant impact as we provide electricity to 25 million customers worldwide. As the world moves towards a net\-zero future, AES is committed to meeting the Paris Agreement's goals by 2050\. Our innovative solutions, such as 24/7 carbon\-free energy for data centers, are setting the pace for rapid, global decarbonization.
If you're ready to be part of a company that's not just adapting to change, but driving it, AES is the place for you. We're not just building a cleaner, more sustainable future \- we're powering it. Apply now and energize your career with a true leader in the global energy transformation.
Who we are:
AI is the new electricity: Just as electricity transformed numerous industries starting 100 years ago, AI is now poised to do the same. We are a small team within AES Next, AES’s new business incubator, focused on building AI companies that accelerate the future of energy. Through our partnership with AI Fund, a venture studio founded by Dr. Andrew Ng, we incubate companies from idea to prototype to venture funding to commercial deployment.
What We’re Looking For:
The ideal candidate is a self\-starter who thrives in fast\-paced environments. Our first goal is to identify and confirm business opportunities by quickly building and early iterating on prototypes with subject matter experts. As successful prototypes are approved to advance in the venture studio process, the role of the product lead evolves from being the primary owner to being an advisor to the new company.
What you will do:
- Collaborate cross\-functionally with business users across AES and with external partners at AI Fund to co\-develop new AI solutions
- Define product requirements by working closely with internal champions to define their pain points and solution needs
- Develop rapid prototypes using AI\-assisted coding tools to test technical feasibility, including front end UX to confirm user/buyer interest (targeting at least 1 early\-stage prototype per quarter)
- Demo prototypes to internal and external users and iterate based on user feedback
- Own overall business case context and the connection between product capabilities and delivering measurable benefits to buyers
- Define scaling and deployment considerations, including mapping necessary integrations and implications for enterprise adoption
- Once prototypes graduate to start\-ups, advise the CEO/CTO on development of the minimal viable commercial product
- For any start\-ups that receive funding, work with the CEO/CTO to support the commercialization and AES deployment of those solutions
What you must bring:
- Experience building applications that incorporate Generative AI
- Hands\-on experience using AI\-assisted coding tools (e.g., Cursor, Claude Code) and understanding of best practices for effective use
- Proficiency with GenAI tools and frameworks\\ Exposure to rapid prototyping methodologies and shipping MVPs
- Exposure to rapid prototyping methodologies and shipping MVPs
- Deep curiosity and a fast\-learning mindset—especially around emerging trends in AI
- Strong communication skills and ability to work collaboratively across disciplines
Nice to have:
- Energy industry experience
- Experience gathering user feedback directly and iterating based on insights
- Interest or experience in product design, product thinking, or new product commercialization
- Familiarity with governance, compliance, and security aspects in AI application architecture
AES is an Equal Opportunity Employer who is committed to building strength and delivering long\-term sustainability through diversity and inclusion. Respecting all backgrounds, differences and perspectives enables us to improve the lives of our people, customers, suppliers, contractors, and the communities in which we live and work. All qualified applicants will receive consideration for employment without regard to sex, sexual orientation, gender, gender identity and/or expression, race, national origin, ethnicity, age, religion, marital status, physical or mental disability, pregnancy, childbirth, or related medical condition, military or veteran status, or any other characteristic protected under applicable law. E\-Verify Notice: AES will provide the Social Security Administration (SSA) and if necessary, the Department of Homeland Security (DHS) with information from each new employee's I\-9 to confirm work authorization.
The expected salary for this position, at commencement of employment, is between $173,000 and $216,750/year; however, base pay offered may vary depending on multiple individualized factors, including market location, job\-related knowledge, skills, and experience. The total compensation package for this position also includes annual bonus. The benefits offered for this position are: medical, dental, and vision coverage, life insurance, 401(k) eligibility, and paid time off (including vacation, sick leave time, and parental leave). Details of participation in these benefit plans will be provided if a candidate receives an offer of employment. If hired, employee will be in an “at\-will position” and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.
Apply by clicking the application link below and submitting your information. The deadline to apply for this role is 07/31/2026
Salary Context
This $173K-$216K 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 AES Corporation, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($194K) sits 8% above the category median. Disclosed range: $173K to $216K.
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.
AES Corporation AI Hiring
AES Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $216K - $216K.
Location Context
AI roles in Los Angeles pay a median of $191,580 across 1,792 tracked positions. That's 4% below 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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