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About This Role
About WalletGyde
WalletGyde is building a Financial AI platform designed to help people better understand, manage, and improve their financial lives through trustworthy, explainable, and personalized financial intelligence.
As we continue evolving our Financial AI architecture, one of our highest priorities is making sure the system does more than generate convincing responses. We want to understand whether every financial answer is correct, grounded in the right data, produced through the right logic, and communicated responsibly.
Over the past year, our team has built a growing reliability infrastructure around our Financial AI, including deterministic financial calculations, Ground Truth datasets, regression testing, financial personas, Guardrails, Tone & Trust standards, QA workflows, failure classification, and what we refer to internally as the WalletGyde Financial Harness.
We are now looking for an AI Systems & Evaluation Engineer Intern to help us strengthen and evolve these systems.
The Role
This is not a traditional machine learning internship focused primarily on model training.
You will work across the systems surrounding modern AI models to help WalletGyde answer one critical question:
How do we know WalletGyde produced the right financial answer, for the right reason, with the right behavior?
You will help investigate where AI failures originate, improve how we evaluate system performance, strengthen our Ground Truth methodology, build regression coverage, and help convert manual evaluation processes into more repeatable engineering systems.
This role sits at the intersection of AI engineering, evaluation, financial logic, QA, data analysis, and responsible AI.
What You Will Work OnGround Truth & Financial Accuracy
You will help audit, validate, version, and expand WalletGyde’s Ground Truth datasets and expected financial answers.
This includes reviewing how expected answers connect to approved financial definitions, formulas, time periods, and deterministic calculation logic.
AI Evaluation
Help improve how WalletGyde measures AI performance using a combination of:
Deterministic evaluators
Model based graders
Expected versus actual comparisons
Financial accuracy testing
Behavioral evaluation
Human review
You will help us move beyond simply asking whether a chatbot response passed or failed and toward understanding why a response succeeded or failed.
Failure Analysis
Investigate failures across multiple parts of the AI stack, including:
Financial data
Date and period logic
Deterministic calculations
Retrieval and RAG
Orchestration and routing
Guardrails
Model responses
Evaluation methodology
The goal is to determine the actual root cause rather than assuming every incorrect response is an LLM problem.
Regression Testing
Help maintain and expand WalletGyde’s regression suite as new edge cases and failures are discovered.
You will work with existing test cases and help ensure engineering improvements do not unintentionally break previously correct behavior.
QA Engineering
Help convert portions of our existing manual QA methodology into repeatable engineering workflows.
This may include test runners, structured evaluation outputs, regression reports, automated checks, and eventually CI/CD evaluation gates.
Guardrail Evaluation
Help build executable tests around behaviors such as:
Uncertainty
Unsupported financial claims
Clarification
Privacy
Financial boundaries
Hallucination prevention
Responsible redirection
Safe handling of incomplete information
Tone & Trust Evaluation
WalletGyde has developed a Tone & Trust framework designed around principles such as:
Never Shame
Never Pretend Certainty
Answer First
Encourage the User
Connect Insight to Action
You will help explore how qualitative standards like these can become measurable, reproducible evaluation criteria.
Financial Harness Engineering
Support the broader WalletGyde Financial Harness connecting:
Financial Data → Deterministic Logic → Ground Truth → Retrieval → AI Reasoning → Guardrails → Tone → Product Response → Evaluation
You will help us understand how these components interact and how failures can be traced through the system.
Evaluation Automation
Work alongside WalletGyde engineers and external technical partners to help move portions of our evaluation methodology into automated workflows, regression pipelines, observability systems, and CI/CD.
What You Will Inherit
You will not be starting from a blank slate.
WalletGyde has already developed significant evaluation infrastructure, including:
140 Ground Truth evaluation questions
26 core regression cases
20 structured financial personas
Financial Metrics & Calculation Logic
Deterministic Financial Intelligence functions
Failure classification methodology
Guardrail framework
Tone & Trust framework
Existing QA workflows
Financial Harness evaluation methodology
Your job will be to understand what exists, identify where it can improve, and help us evolve it into a more mature Financial AI reliability system.
Potential Q4 Projects
Depending on experience and progress, projects may include:
Ground Truth vNext
Help create a cleaner and more versioned Ground Truth structure with validated expected answers, clearer financial definitions, and stronger traceability.
Evaluation Pipeline v1
Help develop a repeatable workflow that runs evaluation cases and captures expected versus actual behavior.
Failure Attribution Framework
Help improve how WalletGyde identifies whether failures originate from data, calculations, date resolution, retrieval, orchestration, Guardrails, model behavior, or evaluation itself.
Regression Suite Expansion
Turn newly discovered failures into permanent regression tests.
Guardrail Evaluation Suite
Help convert key Guardrail requirements into executable tests.
Tone Evaluation Suite
Explore automated and human assisted approaches for evaluating WalletGyde’s communication principles.
Financial Harness Reporting
Help create structured evaluation outputs that can eventually support dashboards, observability, and engineering decision making.
What We Are Looking For
Strong candidates will have experience or interest in several of the following:
Python
SQL
REST APIs
Git and GitHub
Large Language Models
RAG systems
AI evaluation
Automated testing
Data analysis
Prompt evaluation
Model evaluation
AI reliability
Debugging complex systems
CI/CD concepts
Financial data or FinTech systems
Technical documentation
You do not need to be an expert in every area.
We care deeply about your ability to learn quickly, investigate complex systems, ask strong questions, and reason through ambiguous technical problems.
The Kind of Person Who Will Thrive Here
We are looking for someone who is naturally curious.
When an AI response is wrong, your instinct should not be:
“The model made a mistake.”
Your instinct should be:
“Why did this fail, and at what layer did the failure actually begin?”
Maybe the data was incomplete.
Maybe the wrong financial period was selected.
Maybe the deterministic calculation was incorrect.
Maybe retrieval missed a transaction.
Maybe the Ground Truth answer itself was wrong.
Maybe the model received the correct information but explained it incorrectly.
We want someone who enjoys following that chain until the actual cause is understood.
You should also be comfortable reading and understanding existing systems before immediately rebuilding them.
Why This Internship Is Different
Many AI internships focus primarily on prompts, model experimentation, or building chatbot features.
This role focuses on a different question:
How do you engineer trust around an AI system operating in a high stakes domain like personal finance?
You will gain hands on exposure to:
Financial AI architecture
LLM evaluation
Ground Truth engineering
Deterministic financial systems
AI reliability
Responsible AI
Guardrail testing
Regression engineering
RAG systems
FinTech data
Early stage AI product development
Evaluation automation
You will also have the opportunity to see how an early stage company moves from experimental AI systems toward more measurable, observable, and production ready infrastructure.
What Success Looks Like
By the end of the internship, we want you to be able to look at a Financial AI failure and systematically determine:
What happened
Where it happened
Why it happened
How it should be fixed
How we prevent it from happening again
Ultimately, your work will help WalletGyde move from manually asking whether our AI is correct toward building systems that can continuously measure, diagnose, and improve Financial AI reliability.
How to Apply
Please submit your resume along with a brief response to the following:
Tell us about a technical problem you investigated where the first explanation turned out not to be the actual root cause. How did you figure out what was really happening?
GitHub, technical projects, AI evaluation work, research, or other examples of systems you have built or analyzed are encouraged but not required.
WalletGyde, Inc. Building trustworthy financial intelligence through better data, better evaluation, and better AI systems.
Job Type: Internship
Pay: From $100.00 per month
Work Location: Remote
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Walletgyde, 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 $212,000 based on 11,857 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Walletgyde AI Hiring
Walletgyde has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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