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About This Role
Position Summary
The Senior AI Security Engineer is the organizational authority on securing AI\-driven technology. As end\-to\-end application delivery has shifted to large language model (LLM)\-driven development, the traditional SDLC security model no longer applies. This role designs and enforces security across the full AI delivery surface: model configuration and risk, prompt pipelines, agentic workflows, data ingestion and ETL, cloud\-native infrastructure, and the identity and access controls that underpin all of it. The AI Security Engineer embeds security into the build process itself, partners across engineering and data teams, and translates AI\-specific threat knowledge into practical, enforceable controls.
The Senior AI Security Engineer is a full\-time, remote, exempt position and reports to the Sr. Director, Security Architecture and Operations.
Specific Responsibilities
This role spans the full security lifecycle for AI systems from architecture and identity design through build, runtime, and incident response.
- Threat modeling and risk assessment: Lead threat modeling for AI capabilities, agentic features, and integrations. Maintain a living threat model aligned to OWASP Top 10 for LLMs, MITRE ATLAS, and NIST AI RMF, and translate findings into prioritized, actionable controls.
- Secure architecture: Own and maintain the AI security reference architecture covering model hosting, orchestration, tool use, MCP server deployments, and observability. Establish security patterns for new capabilities before they reach production.
- AI model and pipeline security: Evaluate foundation models for data\-leakage risk and supply\-chain provenance. Harden prompt pipelines against injection, jailbreaking, and context poisoning. Secure fine\-tuning pipelines, RAG architectures, and embedding stores with appropriate access controls and poisoning detection.
- Agentic AI security: Define trust boundaries, tool call authorization, privilege scoping, and human\-in\-the\-loop escalation policies for multi\-agent systems. Ensure agents operate under least\-privilege identities with revocable, short\-lived credentials.
- AI\-generated code and build security: Establish review, testing, and gating processes for AI\-generated code across frontend, backend, IaC, and CI/CD. Embed security requirements and policy\-as\-code checks directly into Claude system prompts and project instructions used for development. Enforce SCA/SBOM generation, dependency allowlisting, and secrets management for all LLM\-driven build outputs.
- Identity and access management: Define and enforce identity patterns for both human and non\-human (agent) identities. Apply zero\-trust principles to all model API calls, vector store queries, and external tool invocations. Every request must be authenticated, authorized, and logged. Architect OAuth/OIDC delegation patterns for agentic flows with bounded scopes and session limits.
- Data protection and ETL security: Classify and control data flowing into LLM context windows, vector databases, and training pipelines. Secure ETL and ingestion pipelines against poisoning and schema drift. Enforce masking, tokenization, and access controls to prevent regulated data (PII, PHI, PCI) from appearing in model inputs, outputs, or logs. Extend DLP coverage to LLM prompt submissions and completions.
- Detection, monitoring, and response: Instrument AI infrastructure with behavioral telemetry. Write detection rules for prompt injection attempts, anomalous agent behavior, and data exfiltration through LLMs. Serve as SME for AI\-related security incidents, including model abuse, pipeline compromise, and agentic runaway actions.
- Compliance and audit: Support SOC 2, HIPAA, ISO 27001, and NIST AI RMF compliance as applied to AI systems. Maintain control mappings, evidence, and audit documentation. Evaluate and deploy AI\-specific security tooling (LLM firewalls, guardrail frameworks, agent monitoring) and integrate with SIEM/SOAR.
Skills
- Secure coding knowledge across languages commonly produced by AI\-driven workflows: Python, TypeScript/JavaScript, SQL, and IaC (Terraform/Bicep).
- Strong command of identity protocols: OAuth 2\.0, OIDC, SAML, SCIM, and their application to non\-human identities.
- Prompt engineering sufficient to construct and evaluate adversarial prompts for testing.
- Experience with SIEM, CSPM, and runtime application security tools; ability to write detection logic and correlation rules.
- Data security controls: encryption, tokenization, DLP, and secrets management (HashiCorp Vault, AWS Secrets Manager, Azure Key Vault).
- Scripting and automation skills for building internal security tooling (Python preferred).
- Custom CI/CD security gate development — policy checks written from scratch, not scanner configuration
- Vector database internals (pgvector, Pinecone, Weaviate) — access controls, audit logging, poisoning detection.
- Red team automation for LLMs — adversarial prompt generation and evasion testing at scale
- Deep familiarity with AI orchestration frameworks (LangChain, LangGraph, AutoGen, Claude Agent SDK, or similar) and their security characteristics.
- Engineer programmatic agent kill\-switch and rollback mechanisms triggered by behavioral thresholds
- Track record delivering security architecture artifacts: threat models, reference architectures, security requirements, and control frameworks.
- Build secure memory and context management layers for multi\-agent systems — encryption, access scoping, and TTL enforcement
Experience \& Education
- 8\+ years of progressive information security experience across architecture, application security, identity, and data domains.
- Hands\-on use of Claude for application development, including system prompt design, tool\-use configuration, and multi\-agent orchestration.
- Experience with the Model Context Protocol (MCP) ecosystem and securing MCP server deployments.
- Red team or adversarial AI testing experience: model evasion, adversarial examples, jailbreak research.
- Background in a regulated industry with data sensitivity requirements, healthcare a plus.
- Contributions to AI security open\-source projects, published research, or conference presentations (DEF CON AI Village, Black Hat, NeurIPS).
- Demonstrated success securing AI/ML systems, LLM applications, or agentic AI platforms in a production environment.
- Demonstrated experience building or securing applications developed substantially through LLM\-assisted code generation (Claude, Copilot, Cursor, or equivalent).
- Cloud\-native security experience on at least one major provider (AWS, Azure, GCP), including IAM policy design, network segmentation, and secrets management.
- Demonstrated success automating adversarial testing infrastructure include jailbreak suites, prompt injection harnesses, and regression pipelines tied to model and prompt changes
- Relevant certifications: CISSP, CCSP, OSCP, AWS/Azure Security Specialty, or emerging AI security credentials.
Who We Are
DataSpring is the trusted data connector at the core of healthcare. For more than 25 years, we have powered the industry with the largest and most complete healthcare data foundation in the U.S., including more than 4\.8 million provider data records sourced directly from providers and member data representing 75% of covered lives supplied by health plans. By improving how essential information flows across the system, DataSpring helps healthcare operate more efficiently, accurately, and with greater confidence.
What You Get
At DataSpring, you will do meaningful work at the intersection of healthcare, data, and technology, helping solve complex problems that make the healthcare system work better. You will collaborate with experienced professionals who care deeply about accuracy, trust, and meaningful impact in a fully remote environment.
DataSpring offers competitive compensation and a comprehensive benefits package for full\-time employees, including medical, dental, and vision coverage, a 401(k) with company contributions and matching, paid parental leave, tuition assistance, and generous paid time off. We are committed to investing in our people and supporting professional growth over time.
Equal Opportunity Employer
DataSpring is proud to be an equal opportunity employer and is committed to fostering a workplace where all individuals are valued, respected, and empowered.
Employment decisions at DataSpring are made without regard to race, color, religion, sex, national origin or ancestry, age, marital status, disability, protected veteran status, personal appearance, sexual orientation, gender identity or expression, familial status, family responsibilities, matriculation, political affiliation, genetic information, source of income, place of residence, or any other characteristic protected by law. DataSpring does not tolerate unlawful discrimination or harassment of any kind.
Applicants have rights under the Family and Medical Leave Act (FMLA), Equal Employment Opportunity (EEO), and the Employee Polygraph Protection Act (EPPA). If you need a reasonable accommodation to apply for a posted position, please contact the DataSpring People \& Culture team at Careers@dataspring.org or 202\-517\-0436\.
The pay range for this role is:
180,000 \- 195,000 USD per year(Remote (United States))
Salary Context
This $180K-$195K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Rippling, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($187K) sits 14% below the category median. Disclosed range: $180K to $195K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Rippling AI Hiring
Rippling has 19 open AI roles right now. They're hiring across AI Product Manager, AI Software Engineer, AI/ML Engineer, Data Engineer. Positions span Remote, US, New York, NY, US, San Francisco, CA, US. Compensation range: $60K - $330K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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