AI/ML Engineer II
TENEX.AI · United States
- Hybrid
- Full-time
- $150,000 / year
- United States
Job highlights
- Develop scalable AI systems for cybersecurity.
- Build AI layer for detection and investigation.
- Productionize LLMs and graph reasoning engines.
- Collaborate on ML and rule-based detections.
- Experiment with advanced AI technologies.
About the role
About TENEX
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. Seed round led by Andreessen Horowitz (a16z). As an early employee, you’ll play a meaningful role in defining and building our culture. Get in on the ground floor. We’re a small but well-funded team that just raised a substantial round – joining now comes with limited risk and unlimited upside.
Culture is one of the most important things at TENEX.AI—explore our culture deck at culture.tenex.ai to witness how we embody it, prioritizing the irreplaceable collaboration and community of in-person work.
Role Overview
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.
Location
This role will require Monday - Thursday onsite in any of our locations. WFH Friday.
Job Responsibilities
- Project Execution: Ability to drive and deliver technical components of complex projects. This means communicating effectively to align on requirements, executing on high-quality code, and collaborating with senior engineers and stakeholders throughout the development lifecycle.
- AI Layer Engineering: Design & build the AI layer that powers autonomous detection, RAG-backed investigation, and auto-remediation workflows.
- Productionize Reasoning Engines: Develop and productionize large-scale LLMs, graph-based reasoning engines, and streaming feature pipelines that operate on billions of security events.
- Evaluation & Reliability: Own evaluation & reliability—from prompt libraries and fine-tuning to red-team testing, latency budgets, and fallback strategies.
- Cross-Functional Collaboration: Partner tightly with Product, Detection Engineering, and Customer Success to translate real-world attacker behavior into robust ML and rule-based detections.
- Push the Frontier: Experiment with retrieval-augmented generation, tool-calling agents, and multi-modal models (text + logs + graphs) to keep defenders decisively ahead.
Required Skills & Qualifications
Software Engineering & Architecture Expertise
- Core Engineering: 3-5 years of experience in software development, engineering production systems using modern programming languages (Python, Go, Rust, or Java).
- Agentic Systems: Deep knowledge of agentic systems design, such as Centralized and/or Decentralized MAS (Multi-Agent Systems) architectures.
- Graph Architectures: Solid understanding of Graph structures and specifically graph databases.
- Orchestration Frameworks: Hands-on experience building agents, orchestration frameworks (LangChain/LangGraph, Agno AGI, or custom), and evaluation harnesses.
- Distributed Systems: Deep understanding of microservices architecture, containerization (Docker, Kubernetes), and event-driven systems.
- APIs: Strong fundamentals in API design (REST/gRPC) and distributed systems.
Soft Skills
- Communication: Clear, concise communication skills and a bias for collaborative problem-solving.
- Leadership Alignment: Proven track record of gathering consensus and guiding multi-stakeholder initiatives through uncertain boundaries.
- Analytical Rigor: Strong problem-solving and analytical skills.
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).
Education & Certifications
- 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.
Why Join Us?
- Opportunity to work with cutting-edge AI-driven cybersecurity technologies and Google SecOps solutions.
- Collaborate with a talented and innovative team focused on continuously improving security operations.
- Competitive salary and benefits package.
- 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!
Key skills/competency
- AI ML Engineer
- Python
- Go
- Rust
- Java
- Agentic Systems
- Graph Databases
- Kubernetes
- Cybersecurity
- Machine Learning
Skills & topics
- AI ML Engineer
- Python
- Go
- Rust
- Java
- Agentic Systems
- Graph Databases
- Kubernetes
- Cybersecurity
- Machine Learning
- LLM
- RAG
- MDR
- Startup
- Software Engineering
How to get hired
- Tailor your resume: Highlight experience with Python, Go, Rust, Java, agentic systems, and graph databases for the AI ML Engineer role.
- Showcase AI/ML expertise: Emphasize projects involving LLMs, RAG, and multi-agent systems, aligning with TENEX's AI-native approach.
- Quantify achievements: Use data to demonstrate impact in previous software engineering or cybersecurity roles, especially in startups.
- Prepare for technical interviews: Be ready to discuss distributed systems, Kubernetes, API design, and AI/ML concepts specific to cybersecurity.
- Emphasize collaboration: Discuss your experience working cross-functionally and driving consensus in technical initiatives.
Technical preparation
Behavioral questions
Frequently asked questions
- What is the work arrangement for an AI ML Engineer at TENEX?
- The AI ML Engineer role at TENEX requires Monday through Thursday onsite presence in one of their locations, with Fridays designated for remote work. This hybrid model emphasizes in-person collaboration during the core week.
- What programming languages are essential for the AI ML Engineer position at TENEX?
- Proficiency in modern programming languages such as Python, Go, Rust, or Java is a core requirement for the AI ML Engineer role at TENEX. Python is particularly emphasized for AI/ML development.
- Does TENEX require prior cybersecurity experience for the AI ML Engineer role?
- While prior experience in cybersecurity (SIEM, EDR, SOAR, or MDR) is listed as a 'nice-to-have' for the AI ML Engineer position, a strong foundation in AI/ML and software engineering is the primary requirement. However, cybersecurity domain knowledge can be a significant advantage.
- What kind of AI systems will an AI ML Engineer build at TENEX?
- An AI ML Engineer at TENEX will be instrumental in designing and building the AI layer for autonomous detection, RAG-backed investigations, and auto-remediation workflows. This includes productionizing large-scale LLMs, graph-based reasoning engines, and feature pipelines.
- How important is collaboration for the AI ML Engineer role at TENEX?
- Collaboration is a cornerstone of TENEX's culture. The AI ML Engineer will partner closely with Product, Detection Engineering, and Customer Success teams to translate real-world attacker behavior into effective AI and rule-based detections. Strong communication and consensus-building skills are vital.
- What are the key technical skills for the AI ML Engineer job at TENEX?
- Key technical skills for the AI ML Engineer at TENEX include expertise in agentic systems (MAS architectures), graph databases and architectures, orchestration frameworks like LangChain/LangGraph, and a deep understanding of distributed systems, microservices, and containerization (Docker, Kubernetes).
- What is the educational requirement for the AI ML Engineer position at TENEX?
- A Bachelor's or Master's degree in Computer Science, Engineering, or a related field is required for the AI ML Engineer position. Relevant certifications are considered a plus.
- How can I demonstrate my suitability for the AI ML Engineer role at TENEX?
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