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About This Role
Why Socure?
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Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.
About the Role
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Socure sits on one of the most consequential datasets in the world — a global identity graph spanning hundreds of millions of identities, incorporating PII, device signals, behavioral telemetry, network relationships, and a continuous feedback loop of real\-world fraud and verification outcomes across thousands of clients. The patterns inside this graph tell the story of how fraud evolves: who the adversaries are, how they adapt, and where they're going next.
We are not looking for someone to report what happened. We are looking for someone who can explain why it happened, predict what comes next, and make that story impossible to ignore — for regulators, customers, boards, and the market. This person will sit directly at the intersection of rigorous research, macroeconomic and regulatory intelligence, and the richest identity dataset in the industry.
What You'll Do
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### Own the Research Agenda
Bring the rigor of an applied economist to fraud — proper identification strategies, causal inference, and natural experiments. Design studies and analytical frameworks that produce findings you can stand behind publicly. Answer questions like: What drove the surge in synthetic identity fraud post\-CARES Act? How do adversarial networks respond to model updates? What are the second\-order effects of regulatory changes on fraud displacement across verticals?
### Turn the Identity Graph Into Intelligence
Work across Socure's global identity graph to surface fraud rings, adversarial coalitions, emerging attack typologies, and behavioral shifts before they become industry crises. Extract insights from the full identity stack: PII, device intelligence, email/phone/network signals, behavioral biometrics, and the longitudinal performance feedback flowing back from Socure's clients.
### Bridge the Outside World to Internal Models
Serve as the connective tissue between external signals — regulatory guidance, legislative changes, CFPB and FinCEN trends, macroeconomic shifts that alter fraud incentives, emerging typologies from industry consortia — and Socure's internal research, modeling, and product roadmaps. When a new regulation drops or a fraud pattern surfaces in the press, you already have the data story and the analysis ready.
### Tell Stories That Change Minds
You are not a metrics reporter — you are a narrative architect. Transform complex multivariate findings into white papers that get cited, presentations that land at Money20/20, regulatory briefings that shape policy, and customer insights that create competitive advantage. Your audience ranges from a CRO at a top\-5 bank to a Congressional staffer to a Socure ML engineer, and you'll adjust register without losing substance.
### Build \& Lead a World\-Class Team
Recruit, mentor, and develop a team of researchers, data scientists, and analysts who share your appetite for rigor and storytelling. Create a culture where intellectual curiosity, methodological discipline, and external credibility are the standard.
### Influence Product, Models \& Strategy
Build tight feedback loops with Socure's modeling, product, and engineering organizations so that what you learn in the data becomes new signals, smarter models, better products, and stronger go\-to\-market positioning. You will have a direct line into the strategic roadmap and report directly to the Chief AI \& Innovation Officer.
### Represent Socure Externally
Speak at industry conferences, engage regulatory working groups, author market\-facing research, and participate in customer and partner forums. Your external credibility is a strategic asset — you invest in it and it compounds for Socure.
### Drive Go\-to\-Market Differentiation
Collaborate with Marketing and Growth to develop research\-backed thought leadership that strengthens Socure's brand recognition, supports pipeline generation, and reinforces market leadership. Your insights become a competitive moat.
What You Bring
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- Research Depth \& Econometric Rigor. You approach fraud data the way a serious economist approaches a policy question — with proper identification strategies, an appreciation for confounding, a healthy skepticism of naive correlations, and the discipline to distinguish causation from coincidence. Comfortable with panel data methods, diff\-in\-diff, regression discontinuity, survival analysis, network econometrics, and the full toolkit of applied causal inference.
- Deep Fraud Domain Expertise. You've spent meaningful time in the trenches — synthetic identity, first\-party fraud, account takeover, bust\-out rings, AML\-adjacent typologies, mule networks, or related domains. You understand the adversarial game theory at play and respect the sophistication of the actors on the other side.
- Data Fluency at Scale. You've worked with large\-scale identity, behavioral, or transaction datasets. You understand graph structures, feature engineering at the identity level, and the operational realities of productionizing insights. Conversant in Python, SQL, graph analytics, and ML frameworks.
- External Credibility \& Presence. You have a track record of representing an organization publicly and credibly — major conference appearances, regulatory working groups, authored research the market takes seriously, or a reputation that precedes you in the fraud and identity space.
- Executive Communication Without Dumbing It Down. You can write a 2\-page brief for a CEO that captures all the important nuance, and go 10 levels deep with a PhD data scientist without losing them. You know the difference between simplifying and falsifying, and you never do the latter.
- Regulatory \& Macro\-Intelligence Fluency. You follow the regulatory environment — CFPB rulemaking, FinCEN guidance, state\-level identity legislation, open banking frameworks — and understand how policy changes alter fraud incentives and attack surfaces.
Minimum Qualifications
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- Advanced degree (MS or PhD strongly preferred) in Economics, Statistics, Econometrics, Applied Mathematics, Computer Science, or a related quantitative field
- 10\+ years of applied experience in data science, fraud analytics, risk research, or quantitative economics, with demonstrable impact at scale
- Proven expertise in fraud, identity risk, financial crime, or adjacent domains
- Strong command of causal inference, statistical modeling, and modern ML/AI techniques applied to adversarial or risk problems
- Track record of external thought leadership — publications, conference presentations, regulatory engagement, or equivalent market\-facing credibility
- Exceptional written and verbal communication skills; ability to author compelling, rigorous, market\-facing research
- Experience leading and developing high\-performing technical teams
Preferred Qualifications
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- Experience with graph\-based analytics, identity network modeling, or fraud ring detection using Neo4j, AWS Neptune, or custom graph frameworks
- Familiarity with the regulatory landscape governing identity verification, fraud prevention, and consumer financial protection (CFPB, FinCEN, OCC, state AGs)
- Experience with device intelligence, behavioral biometrics, email/phone/IP signals, or browser fingerprinting in a fraud context
- Published research or white papers in peer\-reviewed journals, industry publications, or prominent market forums
- Experience working in or alongside financial services, fintech, credit bureaus, payments networks, or fraud consortia
- Exposure to explainable AI, model governance, or adverse action frameworks relevant to consumer\-facing decisioning
Reports to: Chief AI \& Innovation Officer
Required Location: SF, NY, Seattle, or Miami
*Please note that we cannot provide sponsorship now or in the future.*
*Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.*
*If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.*
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Compensation Range: $250K \- $300K
Salary Context
This $250K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Socure, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($275K) sits 54% above the category median. Disclosed range: $250K to $300K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Socure AI Hiring
Socure has 12 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span New York, NY, US, Carson City, NV, US, San Francisco, CA, US. Compensation range: $135K - $300K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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