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About This Role
Greenlight is the leading family fintech company on a mission to help parents raise financially smart kids. We proudly serve more than 6 million parents and kids with our award\-winning banking app for families. With Greenlight, parents can automate allowance, manage chores, set flexible spend controls, and invest for their family’s future. Kids and teens learn to earn, save, spend wisely, and invest.
At Greenlight, we believe every child should have the opportunity to become financially healthy and happy. It’s no small task, and that’s why we leap out of bed every morning to come to work. Because creating a better, brighter future for the next generation depends on it.
We are looking for an AI/ML Engineer to join our AI team. The AI/ML Engineer designs, builds, and ships production Generative AI applications, AI agents, and ML systems for customer\-facing and internal products. The role pairs deep expertise in large language models and agentic architectures with senior\-level software engineering to deliver scalable, secure AI solutions. This engineer leads technical design of complex AI systems, drives cross\-functional collaboration with product, platform, and security teams, and shapes the strategic direction of AI/ML capabilities across the organization.
### Your day\-to\-day:
- Design, build, and deploy production AI agents and multi\-agent orchestration systems with prompt engineering, LLM chaining, and tool\-calling patterns for complex, multi\-step workflows.
- Architect RAG pipelines with vector search, hybrid retrieval, and knowledge base management for AI\-driven question\-answering and decision\-support.
- Integrate third\-party AI platforms and LLM providers, designing authentication flows, tool schemas, and agent\-to\-backend communication.
- Design AI agent security architectures including token exchange, delegated access, and user verification flows for systems acting on behalf of users.
- Build production microservices and APIs (FastAPI, Flask, Node.js) serving as orchestration layers and tool endpoints for AI agent systems.
- Architect authentication and authorization for AI services: identity provider integration, token validation, and service\-to\-service auth.
- Deploy, monitor, and maintain ML models and AI agent endpoints on cloud platforms (Databricks, AWS SageMaker) including scaling and health management.
- Build data ETL pipelines for feature engineering, transaction processing, and knowledge base ingestion.
- Develop evaluation and monitoring frameworks for non\-deterministic AI systems: agent correctness testing, retrieval quality, and alerting.
- Author technical design docs, architecture diagrams, and API contracts; mentor junior and mid\-level engineers on AI development practices.
- Lead architecture reviews and produce design documents with implementation roadmaps; evaluate emerging AI technologies to inform team strategy.
- Collaborate cross\-functionally with product, platform, security, and operations to define requirements, prioritize features, and ship AI integrations end\-to\-end.
### What you’ll bring to the team:
- Extensive experience building and deploying AI agents and Generative AI applications in production.
- Deep knowledge of LLMs, agentic architectures, multi\-agent systems, RAG, vector search, tool use/function calling, prompt engineering, and fine\-tuning.
- Hands\-on experience with AI/ML frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent.
- Strong software engineering skills building production microservices and APIs in Python or JavaScript/TypeScript.
- Experience designing auth systems for AI applications: OAuth, token\-based access control, and delegated authorization.
- Proficiency with a major cloud ML platform (Databricks, AWS SageMaker, or Google Vertex AI) for deployment and serving.
- Ability to produce clear technical design documentation and architecture specs for complex systems.
- Strong cross\-functional communication and collaboration across product, engineering, security, and operations.
### Preferred experience:
- Experience with CI/CD pipelines and infrastructure tooling (GitHub Actions, Jenkins, Kubernetes, Terraform).
- Experience with a JVM language (Java, Kotlin, or Scala) for backend service development.
- Background in data pipeline and streaming tools (Airflow, Spark).
Not sure this one’s for you? Don’t count yourself out. Show us what you’ve got and we’ll reach out if there’s a great fit.
### Work perks at Greenlight:
- Medical, dental, vision, and HSA match
- Paid life insurance, AD\&D, and disability benefits
- Traditional 401k with company match
- Unlimited PTO
- Paid company holidays and pop\-up bonus holidays
- Professional development stipends
- Mental health resources
- 1:1 financial planners
- Fertility healthcare
- 100% paid parental and caregiving leave, plus cleaning service and meals during your leave
- Flexible WFH, both remote and in\-office opportunities
- Fully stocked kitchen, catered lunches, and occasional in\-office happy hours
- Employee resource groups
Our stance on salaries:
Greenlight provides a competitive compensation package with a market\-based approach to pay and will vary depending on your location, experience and skill set. The total compensation package for this position will also include a discretionary performance bonus, equity rewards, medical benefits, 401K match, and more. Greenlight conducts continuous compensation evaluations across departments and geographies to ensure we are keeping our pay current and competitive.
The estimated base pay range for this position in (NY, CA, WA): $160,000\- 190,000
The estimated base pay range for this position in (CO): $160,000\- 180,000
Who we are:
It takes a special team to aim for a never\-been\-done\-before mission like ours. We’re looking for people who love working together because they know it makes us stronger, people who look to others and ask, “How can I help?” and then “How can we make this even better?” If you’re ready to roll up your sleeves and help create a world where every child grows up to be happy and healthy in money and life, apply to join our team.
Greenlight is an equal opportunity employer and will not discriminate against any employee or applicant based on age, race, color, national origin, gender, gender identity or expression, sexual orientation, religion, physical or mental disability, medical condition (including pregnancy, childbirth, or a medical condition related to pregnancy or childbirth), genetic information, marital status, veteran status, or any other characteristic protected by federal, state or local law.
Greenlight is committed to an inclusive work environment and interview experience. If you require reasonable accommodations to participate in our hiring process, please reach out to your recruiter directly or email accomodations@greenlight.me.
*We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.*
Salary Context
This $160K-$190K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Greenlight Financial Technology, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($175K) sits 5% above the category median. Disclosed range: $160K to $190K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Greenlight Financial Technology AI Hiring
Greenlight Financial Technology has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $190K - $190K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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