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About This Role
AI Data Quality Analyst (Human\-in\-the\-Loop)
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About the Role
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We are looking for a hands\-on AI Data Quality Analyst (Human\-in\-the\-Loop) to support a strategic client in the commercial property insurance space. This role sits at the intersection of data quality, QA, and product thinking.
You will be the “human in the loop” for an AI\-powered document processing pipeline: reviewing what the AI extracts from complex insurance submissions (e.g., Statements of Values (SOVs), loss runs, spreadsheets, PDFs), correcting errors, and ensuring that downstream tools receive clean, reliable data. On top of the day\-to\-day “grind work” of validation and correction, you’ll zoom out to identify recurring issues, spot patterns, and translate them into clear requirements and bug reports for the engineering team.
This is not a Product Manager role and not a “purely strategic” position. It is very hands\-on, detail\-oriented work that is critical to improving our AI systems and ultimately the client’s underwriting workflow.
What You’ll Do
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- Review AI\-extracted data from insurance submissions (SOVs, loss runs, supporting documents) for accuracy, completeness, and consistency.
- Compare extracted fields against source documents, identify discrepancies, and correct data directly in the appropriate systems or templates.
- Act as a quality gate for the AI pipeline, ensuring output meets agreed business and underwriting expectations before it moves downstream.
- Log issues, defects, and edge cases with clear reproduction steps, examples, and impact, using tools like Jira or similar.
- Identify patterns and root causes behind extraction errors (e.g., recurring issues with specific formats, document types, or fields).
- Translate observed patterns into well\-structured requirements, user stories, and bug reports that engineering and data teams can act on.
- Collaborate closely with architects, data engineers, and other analysts to refine extraction rules, templates, and workflows.
- Use LLMs and AI assistants as tools (e.g., for summarization, cross\-checking, hypothesis generation), while exercising sound judgment about what to trust and what to verify.
- Help continuously improve documentation, checklists, and guidelines for reviewing submissions and extractions.
- Over time, contribute to defining metrics and dashboards for data quality and model performance (e.g., accuracy by field, error rates by document type).
- Work primarily in Eastern European time zones with sufficient overlap to collaborate with US\-based stakeholders.
What We’re Looking For
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### Required Experience \& Skills
- 3\+ years of experience in a data\-intensive role such as data analyst, business analyst, QA analyst, operations analyst, or similar.
- Strong attention to detail and proven experience doing systematic, repetitive data review without loss of quality.
- Excellent analytical skills: ability to trace issues from symptoms (wrong numbers, missing fields) back to likely root causes (document patterns, parsing logic, business rules).
- Demonstrated ability to write clear, structured tickets/requirements for engineering teams (e.g., bug reports, user stories, acceptance criteria).
- Advanced Excel skills (pivot tables, lookups, filters, data cleansing techniques).
- Comfortable working with complex business documents and datasets (financial, insurance, or similar structured data).
- Strong written English for documenting findings, writing tickets, and communicating with distributed teams.
- Comfortable with “grind work”: reviewing many documents/records per day, while maintaining consistency and care.
- Experience with issue\-tracking or project management tools (e.g., Jira, Azure DevOps, Trello, or similar).
- Ability to work independently, manage your own queue, and escalate appropriately when patterns or blockers emerge.
### Nice to Have
- Experience in commercial insurance, property insurance, or financial services operations.
- Familiarity with Statements of Values (SOVs), loss runs, or similar risk/coverage documentation.
- Exposure to LLMs and AI systems (e.g., document intelligence, RAG, chat agents) — as a user, tester, or collaborator.
- Basic familiarity with SQL or other query tools for validating data.
- Prior experience in a Human\-in\-the\-Loop (HIL) or data quality role supporting machine learning models.
- Experience defining or working with data quality metrics (accuracy, completeness, precision/recall, etc.).
### Core Qualities
- Hands\-on ownership: You’re happy to roll up your sleeves, dig into the data, and do what it takes to ensure quality.
- Curiosity \& pattern recognition: You naturally look for trends behind individual issues and want to understand “why” things fail.
- Structured thinking: You can turn messy observations into clear, actionable tickets and requirements.
- Communication \& collaboration: You’re comfortable working with engineers, architects, and business stakeholders across time zones.
- Adaptability: You’re eager to learn new tools, domains, and workflows in a rapidly evolving AI environment.
About Nimble Gravity
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Nimble Gravity is a team of outdoor enthusiasts, adrenaline seekers, and experienced growth hackers. We love solving hard problems and believe the right data can transform and propel growth for any organization.
We work at the cutting edge of data, analytics, and AI, helping clients build and scale solutions that deliver meaningful business impact.
Nimble Gravity is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics, or any other basis forbidden under applicable law. Nimble Gravity considers all qualified applicants.
H1B sponsorship is not available for this position.
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 Nimble Gravity, 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. Mid-level AI roles across all categories have a median of $165,000.
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.
Nimble Gravity AI Hiring
Nimble Gravity has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 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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