Director of AI/ML

TENEX.AI•San Jose, CA

6d•Hybrid

About The Position

As a Director of AI/ML Software Engineering at TENEX, you will be a strategic technical leader responsible for setting the technical vision, managing multiple engineering teams, and driving the execution of scalable, high-performance software and AI systems. You will play a crucial role in shaping the architecture of our AI-driven cybersecurity solutions, fostering technical excellence, and ensuring alignment with the overall product strategy. You’ll collaborate closely with product and design teams, making key technical decisions and helping define how our platform evolves. This is an opportunity to work on challenging engineering problems in security, AI, and distributed systems, while growing into a leadership position as the company scales. Location: This role will require being onsite Monday - Thursday in our San Jose, CA, Sarasota FL or Kansas City, MO office. WFH on Friday.

Requirements

8+ years of progressive experience in software development, including 3+ years managing multiple engineering teams or a significant engineering organization.
Proven track record of defining and executing technical roadmaps, managing budgets, and scaling engineering organizations in a high-growth environment.
Expertise in engineering best practices, agile methodologies, SDLC, and driving organizational process improvements.
Deep technical expertise in designing and engineering scalable, distributed, and production-grade systems using modern programming languages (e.g., Python, Go, Rust, Java, or TypeScript).
10+ years of experience in software development, engineering production systems using modern programming languages (Python, Go, Rust, or Java).
Deep understanding of microservices architecture, containerization (Docker, Kubernetes), and event-driven systems.
Strong fundamentals in API design (REST/gRPC), data modeling, and database technologies (SQL/NoSQL).
Experience with large-scale data processing, analytics, and high-volume transaction systems.
Deep knowledge of LLM architecture, prompt engineering, and Vector database workflows.
Hands-on experience building agents, orchestration frameworks (LangChain/LangGraph, Agno AGI, or custom), and evaluation harnesses.
Exceptional communication, presentation, and negotiation skills, with the ability to articulate technical strategy to executive leadership and external stakeholders.
Strong strategic thinking and analytical skills to solve complex business and technical problems.
Proven ability to mentor, inspire, and grow senior technical talent and leadership within the organization.
A strong passion for cybersecurity and a commitment to building security-first systems and automation.
Clear, concise communication skills and a bias for collaborative problem-solving.
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

Nice To Haves

Prior work in cybersecurity (SIEM, EDR, SOAR, or MDR) or a related domain.
Experience with graph databases or security-focused knowledge graphs.
Familiarity with cloud infrastructure security (AWS, GCP, or Azure) and DevOps practices.
Experience leading engineering efforts for both backend services and complex single-page applications (SPAs) or data visualization tools.
Background leading teams in high-growth startups or enterprise SaaS.
Relevant certifications (e.g., AWS/GCP Professional Engineer, Kubernetes, or security-related credentials) are a plus.

Responsibilities

Set Technical Strategy & Vision: Oversee the design and architecture of scalable, reliable, and secure systems that power our autonomous detection, RAG-backed investigation, and auto-remediation workflows.
Design & Build the AI Layer: Power 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: AI systems—from prompt libraries and fine tuning to red-team testing, latency budgets, and fallback strategies.
Lead & Mentor Engineering Teams: Manage multiple team leads and a diverse cohort of engineers. Drive a culture of technical excellence, accountability, and continuous improvement. Mentor & grow a cohort of AI engineers; run design reviews, uphold code quality, and instill a security-first mindset.
Drive Execution & Delivery: Execute and drive complex software projects across the full stack, ensuring high-quality, on-time delivery while managing technical debt and balancing immediate needs with long-term architectural health.
Own Operational Excellence & Reliability: Establish best practices for system monitoring, performance, accessibility, and security in production environments operating on billions of security events.
Collaborate Cross-Functionally: Collaborate with Product Management, Detection Engineering, and Customer Success to define the product roadmap, translate business needs into technical requirements, and ensure technical investments align with customer value. Partner tightly with Product, Detection Engineering, and Customer Success to translate real-world attacker behavior into robust ML and rule-based detections.
Foster Innovation: Identify and drive the adoption of new technologies, frameworks, and engineering methodologies. Push the frontier—experiment with retrieval-augmented generation, tool-calling agents, and multi-modal models (text + logs + graphs) to maintain a competitive advantage and keep our defenders decisively ahead.

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