Tenex
AI/ML Engineer II
San Jose, CA · Mid
Sponsorship not specifiedDetected 43 days ago
PythonJavaGoRustDistributed SystemsAWSGCPAzureCloud PlatformsDockerKubernetesAPI DevelopmentRESTgRPCMachine LearningLLMsRAGAgentic AILangGraphAI OrchestrationCybersecuritySIEMSOARDetection Engineering
About the role
- Company Overview TENEX is an AI-native, automation-first, built-for-scale Managed Detection and Response (MDR) provider.
- Seed round led by Andreessen Horowitz (a16z).
- We're a small but well-funded team that just raised a substantial round - joining now comes with limited risk and unlimited upside.
Responsibilities
- Design & build the AI layer that powers autonomous detection, RAG-backed investigation, and auto-remediation workflows.
- Develop and productionize large-scale LLMs, graph-based reasoning engines, and streaming feature pipelines that operate on billions of security events.
- Own evaluation & reliability-from prompt libraries and fine-tuning to red-team testing, latency budgets, and fallback strategies.
- Strong fundamentals in API design (REST/gRPC) and distributed systems.
Requirements
- 3-5 years of experience in software development, engineering production systems using modern programming languages (Python, Go, Rust, or Java).
Nice to have
- Domain Background: Prior work in cybersecurity (SIEM, EDR, SOAR, or MDR).
- Startup Mentality: Background driving high-impact engineering initiatives in high-growth startups or enterprise SaaS.
- Cloud Infrastructure: Familiarity with cloud infrastructure security (AWS, GCP, or Azure).
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- Relevant certifications (AWS/GCP Professional Engineer, Kubernetes, or security-related credentials) are a plus.
- Opportunity to work with cutting-edge AI-driven cybersecurity technologies and Google SecOps solutions.
- A culture of growth and development, with opportunities to expand your knowledge in AI, cybersecurity, and emerging technologies.
- If you're passionate about combining cybersecurity expertise with artificial intelligence and have experience with advanced multi-agent architectures, we encourage you to apply!
Skills
- Solid understanding of Graph structures and specifically graph databases.
- Clear, concise communication skills and a bias for collaborative problem-solving.
- Strong problem-solving and analytical skills.
Compensation
- Competitive salary and benefits package.
Company info
- Company Overview
- TENEX is an AI-native, automation-first, built-for-scale Managed Detection and Response (MDR) provider.
- We are a force multiplier for defenders, helping organizations enhance their cybersecurity posture through advanced threat detection, rapid response, and continuous protection.
- Our team is composed of industry experts with deep experience in cybersecurity, automation, and AI-driven solutions.
- Backed by leading investors, we are rapidly growing and seeking top talent to join our mission of revolutionizing the AI-Native MDR landscape.
- We're a fast-growing startup backed by industry experts and top-tier investors led by Crosspoint Capital Partners and also backed by Shield Capital, DTCP (formerly Deutsche Telekom Capital Partners), Deepwork Capital, and the Florida Opportunity Fund.
- As an early employee, you'll play a meaningful role in defining and building our culture.
- Get in on the ground floor.
- As an AI/ML II Engineer at TENEX, you will be a key technical contributor responsible for designing, developing, and optimizing scalable, high-performance AI systems.
- You will play a crucial role in implementing our AI-driven cybersecurity solutions while collaborating across engineering teams and contributing to technical innovation.
- Culture is one of the most important things at http://tenex.ai/TENEX.AI http://TENEX.AI-explore our culture deck at http://culture.tenex.ai/culture.tenex.ai http://culture.tenex.ai to witness how we embody it, prioritizing the irreplaceable collaboration and community of in-person work.
This listing is sourced directly from Tenex's careers page and normalized into a canonical job model.