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About This Role
TLDR: We're looking for an AI Red Team Engineer to break LLM\-powered systems responsibly, automate the repetitive attacks, and turn their findings into clear evidence that powers customer demos, security reviews, and sales conversations. You'll own hands\-on adversarial testing end to end: find the failure, prove it, script it, and write it up.
About us
White Circle is an AI Safety company building the safety, reliability, and optimization layer for AI systems. At the core of our platform are policies – simple natural\-language rules that define what an AI model should and shouldn’t do. We automatically test, enforce, and continuously improve these policies at scale.
- We’ve raised $11M from top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, Sentry, and others
- We process over one hundred million API calls every month
- We fine\-tune and train our own LLMs so they run faster and cheaper than any open or proprietary model
We’re a small, highly focused team. If you want to work deeply on hard problems, see your work ship to production quickly, and influence how AI safety is actually built – you’re the one we need.
You will:
- Red\-team LLM\-powered systems: chatbots, copilots, RAG pipelines, AI agents, tool\-calling workflows, and API\-based AI products.
- Test for jailbreaks, prompt injection, system\-prompt and tool leakage, sensitive\-data and context leakage, unsafe outputs, policy bypass, tool misuse, excessive agency, resource and token\-cost abuse, and business\-logic abuse.
- Write lightweight Python to automate attacks, run prompt sets, call model APIs, collect and score responses, and generate repeatable reports.
- Build and maintain an internal attack library: prompts, scenarios, test cases, regression tests, scoring rubrics, and reusable demo cases.
- Turn model failures into clear reports: what happened, why it matters, how to reproduce it, how severe it is, and how to fix it.
- Convert successful attacks into regression tests and product requirements.
- Track new red\-team and safety techniques and fold the useful ones into our tests.
- Support GTM by producing strong, credible evidence for customer demos, security reviews, and sales conversations.
You'll fit right in if you:
- Genuinely love breaking things and reasoning adversarially.
- Have a background in QA automation, AppSec, API/security/pen testing, or bug bounty.
- Have strong Python scripting skills.
- Have experience testing APIs, web apps, backends, or SaaS products.
- Are hands\-on with LLMs, prompts, system instructions, RAG, agents, and tool/function calling.
- Understand LLM\-specific abuse vectors (prompt injection, jailbreaks, data leakage, tool misuse, excessive agency, token\-cost exhaustion).
- Can find bypasses, abuse edge cases, chain failures, and reason about real\-world impact.
- Can separate real customer risk from low\-impact prompt tricks.
- Write clear, reproducible bug reports in clear English.
- Can move fast without perfect requirements.
- Hold a firm ethical line: you red\-team to make systems safer, operate within scope and the law, and don't produce or traffic in genuinely harmful material.
### A big plus:
- Experience with Burp Suite, Postman, Playwright, pytest.
- Experience with modern LLM red\-teaming automated agents and pipelines.
- Familiarity with LangChain, LangGraph, LlamaIndex, RAG pipelines, AI agents, tool/function calling, and LLM\-as\-judge evaluation.
- Familiarity with OWASP LLM Top 10, OWASP Web Top 10, MITRE ATLAS, or other AI security taxonomies.
- Experience testing RAG systems, AI agents, tool\-calling workflows, browser agents, or internal copilots.
- Experience writing customer\-facing security reports.
- Experience with trust \& safety, abuse prevention, fraud, moderation, or platform security.
- Experience building eval pipelines, regression suites, dashboards, or CI\-friendly security tests.
- A track record in CTFs, red\-team competitions, or responsible\-disclosure / bounty programs.
Why White Circle
- Paid time off in line with your local regulations, no matter where you work from
- Work from Paris (hybrid) \+ relocation package
- Best medical insurance in France
- All the hardware, tools, and services you need
- Covered subscriptions for AI agents
- Team off\-sites twice a year: we've recently been to the Alps and to Saint\-Tropez
How we hire
- Intro call with HR (25 min)
- Take\-home test task
- Technical interview (60 min)
- Final call with CEO (45 min)
Please submit your application in English
Compensation Range: $60K \- $90K
Salary Context
This $60K-$90K range is in the lower quartile 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 White Circle, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($75K) sits 66% below the category median. Disclosed range: $60K to $90K.
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.
White Circle AI Hiring
White Circle has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $90K - $90K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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.
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