Interested in this AI/ML Engineer role at Rippling?
Apply Now →Skills & Technologies
About This Role
About Verra.
*At Verra, we've created a culture of flexibility \+ autonomy. You'll be able to work with remote teams from diverse countries and industries.*
Verra is a global leader helping to tackle the world’s most intractable environmental and social challenges. As a mission\-driven non\-profit organization, Verra is committed to helping reduce greenhouse gas emissions, improve livelihoods and protect natural resources across the private and public sectors. We support climate action and sustainable development with standards, tools and programs that credibly, transparently and robustly assess environmental and social impacts and enable funding for sustaining and scaling up projects that verifiably deliver these benefits. We work in any arena where we see a need for clear standards, a role for market\-based mechanisms, and an opportunity to generate significant environmental and social value.
Verra manages a portfolio of standards, including the:
- Verified Carbon Standard (VCS) \- the world’s leading carbon crediting program, with more than 2000 registered projects in 80 countries, and accounting for two\-thirds of all voluntary carbon market transaction volume.
- Sustainable Development Verified Impact Standard (SD VISta) \- a flexible framework for assessing and reporting on the sustainable development benefits of project\-based activities.
- Climate, Community \& Biodiversity (CCB) Standards \- to identify projects that simultaneously address climate change, support local communities and smallholders, and conserve biodiversity.
- Plastic Waste Reduction Program \- to enable robust impact assessment of new or scaled\-up waste recovery and recycling projects around the globe.
Title: AI Engineer
Location: Remote, US\-Based
Reports to: Senior Director, Technology Solutions
The Opportunity.
We are seeking an AI Engineer to help shape how responsible AI can advance climate action and sustainable development at scale. In this role, you will build practical, trusted AI capabilities that improve how complex environmental and social impact work gets done, enhancing productivity, strengthening decision quality, and enabling human\-centered innovation across Verra’s global workflows.
The Role.
The AI Engineer will translate Verra’s AI priorities into practical, production\-ready capabilities, working across teams to move ideas from concept through implementation, adoption, and ongoing improvement. This work spans the following key areas:
AI\-Driven Decision Support
- Serve as a technical contributor and thought partner for AI solution design
- Collaborate with product managers, software engineers, data teams, business stakeholders, and domain experts to translate operational needs into AI\-enabled capabilities
- Design and develop AI systems that provide context\-aware insights, recommendations, and structured outputs to improve decision quality
- Surface relevant guidance, historical patterns, and knowledge\-based insights within workflows
- Enable human\-in\-the\-loop decisioning, ensuring AI augments expert judgment rather than replaces it
Validation, Risk \& Quality Signals
- Develop AI capabilities that perform pre\-validation and quality checks on data, documents, and submissions
- Identify gaps, inconsistencies, and risk indicators early to improve data quality and reduce downstream rework
- Build models that generate predictive insights and risk signals to support prioritization and focus
Intelligent Workflows \& Automation
- Embed AI into workflows to enable knowledge\-driven execution and decision support
- Leverage enterprise data and prior outcomes to provide relevant context and improve consistency
- Apply AI to automate routine tasks and support workflow execution under controlled conditions
Data, Integration \& Responsible AI
- Integrate AI capabilities into enterprise platforms, workflows, and data ecosystems
- Ensure all solutions are built on trusted, governed, and auditable data sources with clear traceability
- Design AI systems that are transparent, explainable, and aligned with governance and regulatory standards
- Implement safeguards for data integrity, model performance, and appropriate use of AI
Deployment, monitoring, and lifecycle ownership
- Evaluate, test, and monitor AI models to ensure performance, reliability, and appropriate use over time.
- Maintain feedback loops that support continuous improvement based on user experience, model outputs, and business outcomes.
- Apply CI/CD, MLOps, or LLMOps practices to support reliable deployment and lifecycle management.
Our Team.
As part of the Technology Solutions team, you will work with colleagues who are modernizing Verra’s digital infrastructure and building practical tools that help teams deliver high\-quality climate and sustainable development outcomes more efficiently.
- Verrans come from diverse locations and backgrounds, and include carbon market experts, project developers, consultants, climate negotiators, researchers, and auditors.
- We are committed to driving finance at scale to projects and programs that advance climate action and sustainable development through high\-quality standards and programs.
What you’ll bring.
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
- 4 to 8\+ years of experience in software development with at least 2\+ years in AI/ML engineering or applied AI roles.
- Strong programming skills in Python and experience with machine learning frameworks such as PyTorch or TensorFlow.
- Hands\-on experience building, deploying, and maintaining production\-grade AI systems, services, or APIs.
- Experience with large language models (LLM), retrieval\-augmented generation (RAG), prompt engineering, semantic search, vector databases, explainable AI outputs, and modern AI architectures.
- Experience working with complex, multi\-source datasets and designing solutions that support data quality, traceability, and reliable outputs.
- Ability to translate business needs and domain expertise into practical technical solutions in collaboration with subject matter experts.
- Strong communication skills in English, Verra’s standard business language for written and verbal communication.
Preferred qualifications.
- Experience embedding AI capabilities into enterprise workflows or operational platforms.
- Familiarity with data governance, auditability, and regulated environments.
- Experience working with geospatial, environmental, or external datasets.
- Knowledge of workflow automation, process optimization, or decision systems.
- Exposure to platforms involving structured review, validation, or certification processes.
- Experience working in mission\-driven, nonprofit, standards\-setting, climate, sustainability, assurance, or regulated environments.
Physical Demands.
The physical demands described here are representative of those that must be met by a teammate to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- Specific vision abilities required by this job include close vision requirements due to computer work.
Work Environment.
The work environment characteristics described here are representative of those a teammate encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- Ability to sit at a computer terminal for an extended period.
- Access to reliable Wi\-fi with a distraction\-free workspace.
Working at Verra.
Verra is a global leader helping to tackle the world’s most intractable environmental and social challenges. As a mission\-driven non\-profit organization, Verra is committed to helping reduce greenhouse gas emissions, improve livelihoods, and protect natural resources across the private and public sectors.
We’ve created a culture of flexibility \+ autonomy. You’ll be able to work with remote teams from diverse countries and industries. Wherever possible, we aim to find mutually agreeable solutions for international hiring.
The final compensation offered will be contingent upon role, level, and location. Our Talent team can share the specific salary range for your preferred location during the hiring process.
Salary is one component of Verra’s total compensation package which also includes:
- Medical, vision and dental care
- Verra contributions to each employee’s retirement plan/pension scheme with employee and employer contributions subject to statutory regulations
- Paid leave, comprising 22\-30 days vacation, annual sick leave, holidays, and sabbatical after five years of service
Verra is committed to diversity, equity, and inclusion in all our work, and doing this successfully is crucial for us to embody our established values which are Teamwork, Results, Integrity, Balance, and Exploration. We actively celebrate belonging among our team members’ different abilities, sexual orientations, ethnicity, faith, and gender.
How to Apply.
Please send us a cover letter, not to exceed one page, and your resume/CV, not to exceed two pages.
*Verra provides equal opportunity for all job applicants and employees and is committed to providing a work environment free of discrimination. As such, we conduct our recruitment and hiring without regard to race, color, religion, gender identity, sex, sexual orientation, national origin, age, marital status, pregnancy, physical or mental disability, genetics, veteran status, or any other characteristic protected by applicable federal, state, and local law.*
*We will ensure that individuals with disabilities are provided reasonable accommodation to* *participate* *in the job application or interview process, perform crucial job functions, and receive other benefits and privileges of employment. Please contact humanresources@verra.org to request accommodation.*
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Rippling, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Rippling AI Hiring
Rippling has 19 open AI roles right now. They're hiring across AI Product Manager, AI Software Engineer, AI/ML Engineer, Data Engineer. Positions span Remote, US, New York, NY, US, San Francisco, CA, US. Compensation range: $60K - $330K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.