Interested in this AI/ML Engineer role at MathWorks?
Apply Now →About This Role
MathWorks has a hybrid work model that enables staff members to split their time between office and home. The hybrid model provides the advantage of having both in-person time with colleagues and flexible at-home life optimizations. Learn More: https://www.mathworks.com/company/jobs/resources/applying-and-interviewing.html#onboarding.
The Principal AI Security Engineer will serve as an enterprise subject-matter expert for securing AI platforms, agents, integrations, data sources, and supporting infrastructure. This role identifies AI-specific threats, translates risk and governance requirements into practical technical safeguards, and helps teams securely adopt emerging AI capabilities.
The role combines security engineering, architecture review and design, threat modeling, monitoring, and cross-functional collaboration to establish a consistent enterprise approach to secure AI adoption.
MathWorks is on a hybrid schedule (3 days at the Natick, MA Headquarters, 2 days’ work from home)
MathWorks nurtures growth, appreciates inclusivity, encourages initiative, values teamwork, shares success, and rewards excellence.
- Serve as the enterprise security expert for AI platforms, agents, integrations, APIs, and emerging AI use cases.
- Assess AI-related risks, including prompt injection, data exposure, insecure tool invocation, excessive agency, and adversarial inputs.
- Design, implement, and validate security controls for AI platforms, including data protection, access controls, monitoring, guardrails, and approved data-use patterns.
- Conduct security architecture reviews, threat modeling, control assessments, and risk-based evaluations of AI solutions and third-party services.
- Translate policies, governance requirements, and control frameworks into practical technical requirements, implementation plans, and operational processes.
- Develop monitoring, logging, detection, and investigation capabilities for AI activity and integrate relevant telemetry into security operations.
- Partner with Security Operations and incident response teams to improve AI-related detection, investigation, and response processes.
- Collaborate across technical and governance teams to incorporate security requirements into AI platforms, services, and workflows.
- Develop reusable security patterns, implementation guidance, metrics, and evidence to support engineering, governance, risk, and audit activities.
- Monitor changes in AI technologies, threats, and industry guidance, and evolve the organization's security approach accordingly.
- Experience in security engineering, cloud security, data security, application security, detection engineering, or related technical security discipline.
- Experience implementing, operating, or validating security controls such as data protection, DLP, SIEM, cloud security, API security, endpoint security, or application security.
- Experience performing security assessments, architecture reviews, threat modeling, or other risk-based security evaluations.
- Experience translating security risks and governance requirements into practical technical safeguards and implementation plans.
- Strong understanding of data protection principles, including sensitive data discovery, classification, encryption, privacy, and secure data handling.
- Strong analytical, communication, and collaboration skills, with the ability to align technical and nontechnical stakeholders on practical solutions.
- Experience securing AI platforms, AI agents, machine learning systems, or other emerging technologies preferred.
- Knowledge of AI-specific threats such as prompt injection, model misuse, sensitive data exposure, adversarial inputs, and insecure tool invocation preferred.
- Familiarity with AI architectures, APIs, Model Context Protocol services, retrieval-augmented generation, guardrails, and human-in-the-loop controls preferred.
- Experience automating security processes through scripting, APIs, infrastructure as code, or similar approaches preferred.
- Familiarity with NIST AI RMF, NIST Cybersecurity Framework, ISO/IEC 27001, ISO/IEC 42001, or OWASP guidance for AI applications preferred.
- Experience developing reusable security patterns that enable emerging technology adoption while effectively managing risk preferred.
- Relevant AI security or governance certifications, such as ISACA Advanced in AI Security Management (AAISM), IAPP Artificial Intelligence Governance Professional
- A bachelor's degree and 10 years of professional work experience (or equivalent experience) is required.
- Visa sponsorship will not be provided for this position.
- Candidates for this position must be authorized to work in the United States on a full-time basis for any employer without restriction.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At MathWorks, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
MathWorks AI Hiring
MathWorks has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Natick, MA, US, Plano, TX, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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