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About This Role
*Posting Type*
Hybrid
*Job Overview*
WHO WE ARE
Relativity is a leading legal data intelligence company building technology that helps users organize data, discover the truth, and act on it with confidence. Our AI\-powered, cloud platform, RelativityOne, transforms massive volumes of complex information into actionable insights for litigation, investigations, regulatory inquiries, data breach responses, and other high\-stakes legal work where accuracy and trust are crucial.
The world’s largest law firms, corporations, and government agencies rely on Relativity’s legal AI software to securely surface and manage the most relevant and impactful information in their matters. Beyond our commercial impact, we are committed to expanding access to technology and supporting pro bono legal work.
WHAT WE DO
Relativity's AI Security team builds and operates a centralized AI security capability, in collaboration with stakeholders across the company, that enables teams to adopt AI safely and quickly across the lifecycle.
Continuously assess and communicate AI\-related risks and threats through automation, monitoring, and advisory
Develop AI security standards, procedures, and guidelines
Embed AI security into engineering deliveries
Provide product teams with AI security guidance and requirements
ABOUT THE ROLE
As a Senior AI Security Engineer, you will play a hands\-on role at the center of Relativity’s AI security efforts. You will lead AI security reviews, threat modeling, and tooling initiatives that ensure Relativity's growing AI ecosystem—including agentic systems, LLM integrations, and AI\-assisted workflows—remains secure in a rapidly evolving environment.
*Job Description and Requirements*
WHAT YOU’LL DO* Conduct hands\-on AI security reviews and threat modeling using frameworks such as STRIDE, MITRE ATLAS, and NIST AI RMF
- Propose and implement safeguards for software products, tools, and infrastructure prior to production release
- Evaluate, build, configure, and modernize first\- and third\-party AI security tooling, scanning, automation, and agentic workflows
- Strengthen the overall security posture by hardening AI systems and leveraging AI to enhance security capabilities
- Respond to customer AI security inquiries and support audit preparation and audit activities
- Deliver clear written and verbal reports and recommendations to both technical and non\-technical stakeholders
- Lead engagements and manage stakeholder relationships across distributed teams
- Stay current on AI security developments and engineering practices and share knowledge across internal teams
WHAT WE’RE LOOKING FORRequired* Bachelor’s degree in Computer Science, Cybersecurity, Information Systems, or a related field, or equivalent experience
- 5\+ years of commercial experience in software and/or security engineering, with strong computer science fundamentals
- 2\+ years conducting product security reviews and threat modeling using frameworks such as MITRE ATT\&CK / ATLAS, NIST AI RMF, or STRIDE
- 3\+ years of hands\-on experience with cloud platforms (Azure preferred; AWS or GCP acceptable)
- Proven ability to deliver large\-scale, cross\-functional projects
- Strong written and verbal communication skills with the ability to explain complex security concepts to varied audiences
- Strong analytical and problem\-solving skills with a proactive, ownership\-driven mindset
- Passion for helping teams adopt AI safely and championing AI security best practices
- Experience working effectively with geographically distributed teams
Preferred* Master’s degree in Computer Science, Cybersecurity, or a related field, or equivalent experience
- Experience in the legal or e\-discovery domain and familiarity with litigation workflows
- Experience operating in a global SaaS environment
- Experience with commercial AI technologies (e.g., Copilot, ChatGPT, Gemini) and understanding of AI\-specific attack vectors
- 1\+ year designing or operating AI\-based security capabilities and protecting autonomous AI workflows
- Proficiency in at least one modern object\-oriented programming language (e.g., C\#, Python, Java)
- Relevant certifications such as Microsoft Certified: Azure Security Engineer Associate (AZ\-500\)
WHY WE COULD BE A GREAT FITImpactful Mission
- Build systems that help customers organize data, discover the truth, and act on it in high\-stakes legal matters.
Engineering at Scale
- Work on distributed, cloud\-native systems that process large volumes of data.
Cutting\-Edge Technology
- Build with AI, cloud platforms, and scalable architectures shaping legal tech.
Growth and Ownership
- Gain experience owning systems end\-to\-end across cloud and distributed environments.
Collaborative Culture
- Work in a team focused on knowledge sharing and continuous improvement.
Inclusive Environment
- Diverse perspectives create stronger teams and better outcomes.
Compensation and Benefits
- Competitive salary, benefits, DTO, parental leave, and equity program.
Relativity is committed to competitive, fair, and equitable compensation practices.
This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long\-term incentives.
The expected salary range for this role is between following values:
$130,000 and $195,000
The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.
Required Skills:
Access Management, Application Security, Endpoint Security, Network Security, Penetration Testing, Security Architecture Design, Security Information, Security Information and Event Management (SIEM), Security Operations, Vulnerability Management
Salary Context
This $130K-$195K range is below 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 Relativity, 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 ($162K) sits 26% below the category median. Disclosed range: $130K 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.
Relativity AI Hiring
Relativity has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in IL, US. Compensation range: $195K - $195K.
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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