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About This Role
### About Ethos
Ethos is a leading life insurance technology company on a mission to protect families by democratizing access to life insurance and empowering agents at scale. With its robust three\-sided technology platform, Ethos is transforming the life insurance experience for consumers, agents, and carriers alike. Ethos offers instant, accessible products and a seamless online process that requires no medical exams and just a few health questions; it eliminates traditional barriers, making it easier than ever for everyone to protect their families. Ethos is redefining how life insurance is bought, sold, and underwritten.
About the role
We are looking for a skilled and creative AI Red Team Engineer to join our offensive security team. In this role, you will simulate real\-world adversaries, exploit vulnerabilities across applications, cloud infrastructure, and AI/ML systems using both traditional penetration testing techniques and cutting\-edge AI\-augmented attack tooling.
You will operate across the full attack surface: web apps, APIs, mobile, internal networks, and AI\-powered products including LLM pipelines, model APIs, agents, and RAG systems. You will help us find the flaws before the adversaries do, and work closely with engineering and product teams to close those gaps.
Duties and Responsibilities:
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AI \& LLM Security Testing
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- Design and execute adversarial attacks against large language model (LLM)\-powered products including prompt injection, jailbreaking, goal hijacking, and context manipulation.
- Test retrieval\-augmented generation (RAG) pipelines for data exfiltration, poisoning, and unauthorized knowledge extraction.
- Assess AI agent systems and agentic workflows for unsafe tool\-use, privilege escalation, and indirect prompt injection via environment feedback.
- Conduct model extraction, membership inference, and adversarial example attacks against deployed ML models.
- Evaluate AI guardrails, safety filters, and content moderation layers for bypass techniques.
Penetration Testing \& Ethical Hacking
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- Perform full\-scope penetration tests across web applications, REST/GraphQL APIs, mobile apps (iOS/Android), cloud environments (AWS, GCP, Azure), and internal networks.
- Conduct red team exercises simulating advanced persistent threat (APT) actors using MITRE ATT\&CK and AI\-augmented techniques.
- Exploit vulnerabilities across the OWASP Top 10 and beyond: SSRF, IDOR, XXE, SSTI, authentication bypasses, and logic flaws.
- Perform social engineering and phishing simulations as part of combined red team campaigns.
- Conduct cloud and Kubernetes security assessments including IAM misconfigurations, container escapes, and privilege escalation paths.
AI\-Augmented Attack Operations
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- Leverage AI models and tools (e.g., LLMs, code generation, fuzzing assistants) to accelerate vulnerability discovery, payload crafting, and exploit development.
- Build or adapt AI\-powered reconnaissance, exploitation, and evasion tooling for internal use in red team engagements.
- Stay current with adversarial AI research and translate academic findings into practical red team techniques.
- Use AI to automate repetitive testing tasks and generate novel attack variants at scale.
Qualifications and Skills:
- 7\+ years of hands\-on penetration testing and offensive security experience in a professional setting
- Demonstrated experience testing AI/ML systems, LLM\-powered products, or AI APIs
- Experience conducting red team engagements
- Scripting and tool development
- Strong understanding of authentication protocols and common implementation flaws
- Familiarity with cloud security architectures and common misconfigurations
- Working knowledge of Docker/Kubernetes and container security
- Understanding of LLM architectures and how they relate to attack surfaces.
- Familiarity with OWASP LLM Top 10
- Practical experience with prompt injection and jailbreak techniques against LLMs
- Ability to use LLMs as force\-multipliers in red team workflows
### Preferred Qualifications
- Certifications: OSCP, OSEP, CRTO, CRTE, PNPT, CEH, GPEN, GWAPT, or equivalent
- Experience with adversarial ML frameworks
- Contributions to open\-source security tooling or published CVEs / bug bounty hall\-of\-fame credits
- Familiarity with AI governance frameworks
- Experience with GenAI infrastructure
- Background in threat modeling for AI\-powered applications
- Reverse engineering skills for binary and mobile assessments
- CTF participation or competitive hacking experience
\#LI\-Remote \#LI\-MK1
The US national base salary range for this full\-time position is $152,000 \- $269,000\. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job\-related skills, experience, and relevant education or training.
Please note that the compensation details listed in US role postings reflect the base salary only and do not include applicable bonus, equity, or benefits.
You can find further details of our US benefits at https://www.ethoslife.com/careers/
Don't meet every single requirement? If you're excited about this role but your past experience doesn't align perfectly with every qualification in the job description, we encourage you to apply anyway. At Ethos we are dedicated to building a diverse, inclusive and authentic workplace.
We are an equal opportunity employer.. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Pursuant to the SF Fair Chance Ordinance, we will consider employment for qualified applicants with arrests and conviction records.
To learn more about what information we collect and how it may be used, please refer to our California Candidate Privacy Notice.
*Recruitment Notice: Please be aware of recruitment scams. All legitimate communication from our team will only come from email addresses ending in @ethos.com or @getethos.com.*
*We will never ask for payment, banking details, or sensitive personal information during the hiring process. If you are contacted by someone claiming to represent us from a different email address, please treat it as fraudulent.*
Salary Context
This $152K-$269K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Ethos Life, 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($210K) sits 14% above the category median. Disclosed range: $152K to $269K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Ethos Life AI Hiring
Ethos Life has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $269K - $269K.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 positions. About 14% of all AI roles offer remote work.
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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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