Company Description
Dallas Venture Capital is leading this search on behalf of one of our portfolio companies, a stealth-mode startup redefining network security. The company is co-backed by Khosla Ventures. The perimeter model was built for a world that no longer exists. Threats move faster than rules can be written, and signal volume far exceeds what any human team can process. The company is building an AI-native security platform from the ground up rather than layering AI onto legacy tooling. The founding team consists of repeat operators who have built and exited two prior security companies together, with deep backgrounds in real-time analytics, large-scale data infrastructure, and security engineering.
Role Description
The Lead AI Engineer will design, build, fine-tune, and deploy the AI/ML systems at the core of the product. The focus is on Small Language Models (SLMs) and domain-specific models for security use cases including threat detection, anomaly detection, traffic classification, policy automation, and risk scoring. This is a hands-on technical leadership role working directly with the founders and the engineering, product, and security teams.
Qualifications
- Strong hands-on experience in AI, Machine Learning, Deep Learning, and NLP.
- Experience creating, fine-tuning, evaluating, and deploying SLMs or domain-specific models.
- Strong Python skills with frameworks such as PyTorch, TensorFlow, Hugging Face, or scikit-learn.
- Experience with model fine-tuning, distillation, embeddings, RAG, and inference optimization.
- Experience with cloud platforms such as AWS, GCP, or Azure.
- Familiarity with MLOps, model monitoring, data pipelines, and vector databases.
- Strong communication, problem-solving, and technical leadership skills.
- Cybersecurity, network security, or security analytics experience strongly preferred.
- Master's or Ph.D. in Computer Science, AI, ML, Data Science, or a related field preferred.
Responsibilities
• Build, fine-tune, and deploy Small Language Models (SLMs) for cybersecurity use cases.
• Develop AI/ML models for threat detection, anomaly detection, policy recommendations, and security analytics.
• Create training datasets, evaluation benchmarks, and model validation workflows. • Optimize models for accuracy, latency, cost, and production scalability.
• Build AI pipelines for data ingestion, model training, fine-tuning, deployment, and monitoring.
• Partner with engineering teams to integrate AI capabilities into production systems.
• Evaluate emerging AI, SLM, LLM, and cybersecurity modeling techniques.
• Mentor engineers and help establish AI/ML engineering best practices.
Who Thrives Here
- People genuinely excited about AI as a paradigm shift, not a feature.
- People energized by early-stage ownership and ambiguity.
- People with real depth in security, infrastructure, or ML.
If you match the above criteria and are excited about the opportunity, please send your resume and portfolio to [email protected].