AI Security Engineer

ID: 9425

Type: Full-time

Category: Others

Company Name: YipitData

Location: United States

Education Level: Senior (5-10 years)

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Job Description

About Us:

YipitData is the leading market research and analytics firm for the disruptive economy and recently raised up to $475M from The Carlyle Group at a valuation over $1B.

We analyze billions of alternative data points every day to provide accurate, detailed insights on ridesharing, e-commerce marketplaces, payments and more. Our on-demand insights team uses proprietary technology to identify, license, clean and analyze the data many of the world’s largest investment funds and corporations depend on.

For three years and counting, we have been recognized as one of Inc’s Best Workplaces. We are a fast-growing technology company backed by The Carlyle Group and Norwest Venture Partners. Our offices are located in NYC, Austin, Miami, Los Angeles (CA), Cupertino (CA), Hong Kong, Shanghai, Beijing, Guangzhou, and Singapore. We cultivate a people-centric culture focused on mastery, ownership, and transparency. About the Role:

We are seeking an AI Security Engineer to lead the implementation, monitoring, and continuous improvement of security, governance, and trust controls for AI systems across the organization. This role will focus on operationalizing AI system security controls using the Agentic Trust Framework mapped to OWASP guidance and the NIST AI RMF, with particular emphasis on observability engineering, behavioral monitoring, policy enforcement, misuse detection, and risk-informed response.

This person will serve as a bridge between Security, Engineering, Data, Platform, Compliance, and AI product teams to ensure AI systems are not only functional and performant, but also trustworthy, auditable, resilient, and aligned with enterprise governance requirements.

The ideal candidate combines technical depth in AI/ML systems, strong security and monitoring instincts, and the ability to define practical controls for complex, fast-evolving agentic and generative AI environments.

We expect U.S. based working hours with the majority of the team working East and Central Time Zones.

In this role, you will:

Own AI behavior monitoring: Define what trustworthy and untrustworthy AI behavior looks like, and ensure it is measurable in production.
Own AI observability standards: Establish telemetry, tracing, logging, and alerting requirements for AI systems and agentic workflows.
Own control validation for agentic systems: Verify that guardrails, policy checks, access boundaries, and execution constraints are functioning as intended.
Own AI security event analysis: Detect, investigate, and document suspicious, unsafe, or non-compliant AI behaviors and coordinate response.
Own implementation support for governance frameworks: Translate governance principles into technical and operational requirements that product and platform teams can adopt.
Own AI trust metrics and reporting: Define KPIs, KRIs, and dashboards that show leadership whether AI systems are operating within approved trust and security boundaries.
Own continuous improvement of AI controls: Use incidents, testing, behavioral findings, and stakeholder feedback to strengthen control design and reduce residual risk over time.

You Are Likely To Succeed If:

5+ years of experience in one or more of the following: security engineering, detection engineering, observability engineering, site reliability engineering, application security, ML platform engineering, or AI governance implementation.
Experience designing monitoring, logging, telemetry, or detection strategies for distributed systems, cloud services, or data-intensive applications.
Familiarity with AI/ML system architecture, including large language models, retrieval-augmented generation, inference pipelines, model APIs, and agentic workflows.
Experience translating governance, risk, or policy requirements into operational controls and measurable technical requirements.
Strong understanding of security concepts such as identity and access management, least privilege, data protection, abuse prevention, auditability, and incident response.
Experience investigating system behavior, identifying anomalies, and working cross-functionally to drive remediation.
Hold industry certifications (or equivalent experience): CISSP, CCSP, GIAC Machine Learning Engineer (GMLE)
Strong written communication skills, including ability to write standards, control definitions, runbooks, and leadership-facing summaries.

Preferred Qualifications:

Experience with AI observability tooling, tracing frameworks, or telemetry pipelines for LLM or agent-based systems.
Experience implementing controls for AI safety, AI red teaming, prompt security, model misuse detection, or secure tool execution.
Familiarity with Microsoft security, compliance, and AI governance ecosystems.
Familiarity with trust and safety concepts for generative AI and autonomous systems.
Experience supporting internal governance, risk, privacy, or compliance review processes for AI-enabled products.
Experience building dashboards, alerts, and behavioral analytics for security or operational monitoring.
Experience working in highly regulated or audit-sensitive environments.

What We Offer:

Our compensation package includes comprehensive benefits, perks, and a competitive salary:
- We care about your personal life, and we mean it. We offer flexible work hours, flexible vacation, a generous 401K match, parental leave, team events, wellness budget, learning reimbursement, and more!
- Your growth at YipitData is determined by the impact that you are making, not by tenure, unnecessary facetime, or office politics. Everyone at YipitData is empowered to learn, self-improve, and master their skills in an environment focused on ownership, respect, and trust. See more on our high-impact, high-opportunity work environment above!
- The annual on-target earnings for this position is anticipated to be up to $230 ~ $280K. The final offer may be determined by a number of factors, including, but not limited to, the applicant's experience, knowledge, skills, abilities, as well as internal team benchmarks.
The compensation package also includes equity.

This role may be performed fully remotely within the United States. Please note that our US headquarters are located in NYC. We also have have US offices in Austin, Miami, Los Angeles (CA), and Cupertino (CA). If the remote work is performed outside of these offices, income may be subject to New York State tax withholding.

Please note that for this position, we are not able to consider candidates who currently or in the future will require visa sponsorship.

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal-opportunity employer.

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Company Information

Company Name: YipitData

Company Website: https://www.yipitdata.com

Company Address: N/A

YipitData is a technology-driven data and analytics company that specializes in collecting, cleaning, standardizing, and delivering alternative data to institutional investors, corporate strategy teams, and other professional decision-makers. The company aggregates high-frequency, company- and category-level signals derived from large-scale public and licensed online sources and consumer transaction streams, and packages those signals into commercial data products, API feeds, dashboards, and bespoke research projects. YipitData’s services are intended to help clients monitor market trends, evaluate company performance between reported results, and generate timely insights that complement traditional financial and market data. Core business activities - Data collection and engineering: YipitData operates automated pipelines and scalable engineering processes that ingest vast quantities of web data (including publicly accessible listings, product pages, marketplace activity, app store metadata, and other internet-visible signals) and licensed consumer transaction data. These ingestion systems emphasize normalization, deduplication, enrichment, and long-term storage to produce reliable time-series signals. - Data cleaning, categorization, and transformation: The firm applies a combination of deterministic and statistical methods, natural language processing, and human-led quality control to categorize merchants, product SKUs, and user-level measures into standardized schemas. This work turns raw, heterogeneous inputs into usable metrics (for example, merchant-level revenue proxies, product- or category-level sales trends, or store-level activity counts) suitable for quantitative analysis. - Productization and delivery: YipitData packages its derived signals into commercial offerings—pre-built datasets, API endpoints, dashboards, and regularly updated reports—that clients can consume directly or integrate into existing workflows. Products are designed for different use cases such as hedge fund trading signals, portfolio monitoring, corporate benchmarking, due diligence, and market research. - Custom research and client services: In addition to off-the-shelf products, YipitData provides bespoke research engagements and consulting to build custom data extracts, bespoke KPIs, or tailored analytics for individual clients. These engagements typically combine YipitData’s data engineering with subject-matter expertise to answer specific investment or business questions. Main products and services YipitData’s offerings are focused on delivering alternative data products and analytics. Core service categories include: - Aggregated and normalized datasets: Time-series datasets that summarize consumer behavior, e-commerce activity, marketplace dynamics, and industry-specific measures at merchant, brand, product, or geographic granularity. These datasets aim to provide earlier or higher-frequency visibility into revenue-related trends than quarterly financial reports. - Transactional and consumer-spend signals: Derived metrics based on aggregated, anonymized transaction-level data and panel-based spending streams. These signals are used to estimate retail sales, marketplace take rates, category demand, and other economic indicators. - Web-scraped and marketplace intelligence: Structured feeds and indexes created from continuous scraping of public web sources—such as product listings, pricing, inventory, reviews, and seller activity—used to track pricing dynamics, assortment changes, and marketplace health metrics. - APIs and data feeds: Programmatic access to updated signal streams enabling quantitative teams to integrate YipitData’s metrics directly into models, backtests, and internal analytics platforms. - Dashboards and thematic reports: Analyst-curated dashboards and periodic research notes that synthesize the company’s signals into actionable narratives around sectors such as e-commerce, travel, food delivery, retail, and online marketplaces. - Custom research projects and advisory: Tailored analyses and one-off deliverables designed to support specific investment theses, M&A diligence, corporate competitive intelligence, or operational benchmarking. Clients and use cases YipitData serves institutional clients including hedge funds, asset managers, private equity firms, corporate strategy and investor relations teams, and consulting firms. Typical uses include short-term signals for trading, cross-checking company-reported metrics, revenue and customer-growth estimates, sector monitoring, and operational benchmarking. The data is often used to complement traditional datasets (financial statements, sell-side research, and industry reports) by providing higher-frequency inputs and alternative perspectives. Data governance and privacy stance YipitData emphasizes compliance and governance in its data collection and delivery processes. The firm states that it relies on publicly accessible sources and licensed datasets, and implements aggregation, anonymization, and privacy-preserving transformations to ensure that delivered products are designed for institutional use without exposing personally identifiable information. YipitData also documents methodologies around signal derivation, coverage, and known limitations to help clients interpret the data correctly. Technology and delivery model YipitData combines scalable data engineering infrastructure (for collection, storage, and processing) with analytical tooling and client-facing delivery mechanisms such as APIs, managed feeds, and dashboards. The company positions itself as a data-as-a-service provider in the alternative-data market, offering both standardized products for common use cases and bespoke services for deeper, vertical-specific insights. Technical clients typically integrate YipitData feeds directly into quantitative workflows, while non-technical users consume insights through analyst briefings and visual dashboards. Positioning in the market Within the broader alternative data ecosystem, YipitData is recognized as a provider focused on consumer and web-sourced signals that are directly applicable to revenue and transaction-level analysis. The company differentiates itself by emphasizing high-frequency, company-level observability and by offering both raw and processed signals together with methodological transparency and client support. This positioning makes its products appealing to market participants seeking timely indicators of company performance and industry trends without building equivalent internal data pipelines. Limitations and interpretation YipitData’s signals are proxies and should be interpreted in context: coverage and signal quality vary by sector and geography, and derived metrics are subject to sampling biases inherent in the underlying data sources. The company provides documentation and client support to help users understand signal construction, coverage, and appropriate use cases. YipitData’s offerings are designed to augment, not replace, traditional financial analysis, and best results are generally achieved by combining alternative signals with fundamental research and other data sources.

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