Most boards and executives are currently flying blind when it comes to cyber risk. They are guessing. At Safe, we’ve built an AI-driven engine that finally gives the C-Suite a clear, quantified, and real-time view of their security posture. We don’t just provide data; we provide certainty.
We are a $170M Series C-funded category leader. We don’t play in the mid-market; we operate at the highest levels of global enterprise. Today, we are proud to serve 10% of the Fortune 500, protecting global icons such as Apple, Netflix, AT&T, Verizon, and Victoria’s Secret.
As we scale toward our next chapter, we are looking for high-performers who want to do the best work of their careers at the intersection of AI and Cybersecurity.
The Culture Memo: Our Operating SystemSafe is not a typical corporate environment. We are a high-intensity, mission-driven team. We value builders who want to define a category and work alongside people who are equally committed to excellence.
Extreme Ownership: We don’t do "not my job." We hire people who see a gap and own the solution from start to finish.
The Elite Standard: We serve the most sophisticated companies on the planet. Our work must be bulletproof. Whether it’s a line of code or a sales deck, we aim for Tier-1 quality every time.
Methodology & Rigor: We don’t wing it. From Force Management and MEDDICC in sales to data-driven sprints in engineering, we rely on proven frameworks to stay disciplined and predictable.
Radical Candor: We move too fast for politics or sugar-coating. We value direct, honest feedback that helps us find the right answer quickly.
The Series C Hustle: We have the stability of a well-funded leader but the heart of a startup.
We want our team to feel like owners because they are owners. We trust our people to manage their results and their time.
Meaningful Equity: Every "Safestar" is a shareholder. You aren’t just an employee; you are a partner in our success.
Unlimited Leaves: We don’t believe in clock-watching. We offer unlimited leave because we trust you to take the time you need to recharge while staying committed to the mission.
Comprehensive Benefits: We provide top-tier medical insurance and wellness benefits to ensure you and your family are well cared for.
Career Trajectory: We are growing aggressively. For high-performers, the path for advancement moves at the speed of your ambition.
Core Responsibilities:
- Architect Safe’s AI Systems: Design and scale AI-driven components — LLM orchestration, retrieval-augmented generation (RAG), vector stores, prompt pipelines, and AI microservices. Drive architecture for AI observability, safety, and evaluation (precision, recall, F1, hallucination detection, cost metrics).
- Productionize AI Agents: Build multi-turn, goal-oriented agent systems that automate reasoning across TPRM, CTEM, and CRQ domains (e.g., control reviews, issue RCA, automated responses). Ensure reliability, traceability, and deterministic behavior in production.
- AI Infrastructure & Platform Ownership: Partner with Platform & DevOps teams to operationalize model serving (AWS SageMaker, Bedrock, or self-hosted Llama), build AI APIs, and manage model lifecycle and versioning. Establish feature stores, embedding management, and in-memory retrieval layers.
- Data Pipeline & Knowledge Graph Integration: Work with Data Engineering to design pipelines for structured and unstructured data ingestion, semantic indexing, and context retrieval (Snowflake + Iceberg + LlamaIndex).
- AI Evaluation, Monitoring & Governance: Define internal frameworks for golden dataset validation, LLM evaluation (LangFuse/LangSmith), and safety enforcement policies. Implement human-in-the-loop (HITL) mechanisms and continuous feedback loops.
- Mentor & Multiply: Guide AI and backend engineers on architectural design, experimentation methodologies, and prompt optimization. Collaborate with product leaders to translate abstract AI goals into measurable engineering deliverables.
Minimum Qualifications:
- Experience: 12+ years total experience in software engineering, including 4+ years building AI/ML systems or large-scale data/LLM infrastructure.
- Core Technical Skills:
- MLOps & Infra: Familiar with model versioning, CI/CD for ML, and performance optimization for real-time inference.
- Applied AI Focus: Practical understanding of evaluation metrics, hallucination detection, RAG reliability, and enterprise AI safety.
Preferred Qualifications:
- Experience integrating AI into cybersecurity or risk management products
- Familiarity with multi-agent systems and autonomous workflows (CrewAI, LangGraph, AutoGen)
- Experience building AI evaluation dashboards and AI observability stacks
- Knowledge of knowledge graphs, semantic search, or retrieval pipelines
- Exposure to data governance, compliance, or SOC2/ISO 27001 environments
- Published research, open-source contributions, or prior leadership of AI teams is a strong plus
Skills Required
- 12+ years total experience in software engineering
- 4+ years building AI/ML systems or large-scale data/LLM infrastructure
- Strong programming fundamentals in Python, Go, or TypeScript
- Deep understanding of LLM architectures, prompt engineering, and RAG pipelines
- Hands-on experience with LangChain, LlamaIndex, or equivalent orchestration frameworks
- Experience with vector databases (FAISS, Pinecone, Weaviate, Redis Vector, Milvus)
- Cloud model deployment experience (AWS SageMaker, Bedrock, Vertex AI, or custom inference APIs)
- Familiarity with data systems: Snowflake, Iceberg, S3, Postgres/MySQL
- Knowledge of MLOps practices: model versioning, CI/CD for ML, performance optimization for real-time inference
- Applied AI skills: evaluation metrics, hallucination detection, RAG reliability, enterprise AI safety
- Experience mentoring and guiding engineers on architecture, experimentation, and prompt optimization
- Experience integrating AI into cybersecurity or risk management products
- Familiarity with multi-agent systems and autonomous workflows (CrewAI, LangGraph, AutoGen)
- Experience building AI evaluation dashboards and observability stacks
- Knowledge of knowledge graphs, semantic search, or advanced retrieval pipelines
- Exposure to data governance, compliance, SOC2 or ISO 27001 environments
- Published research, open-source contributions, or prior leadership of AI teams
What We Do
Safe Security is a pioneer in the “Cybersecurity and Digital Business Risk Quantification” (CRQ) space. It helps organizations measure and mitigate enterprise-wide cyber risk in real-time using it’s ML Enabled API-First SAFE Platform by aggregating automated signals across people, process and technology, both for 1st & 3rd Party to dynamically predict the breach likelihood (SAFE Score) & $$ Value at Risk of an organization Headquartered in Palo Alto, Safe Security has over 200 customers worldwide including multiple Fortune 500 companies averaging an NPS of 73 in 2020. Backed by John Chambers and senior executives from Softbank, Sequoia, PayPal, SAP, and McKinsey & Co., it was also one of the Top Contributors to the National Vulnerability Database(NVD) of the U.S. Government in 2019 and the ATT&CK MITRE Contributor in 2020. The company, since 2018, has also been working with MIT in joint research for the development of their SAFE Scoring Algorithm. Safe Security has received several awards including the Morgan Stanley CTO Innovation Award.
