As an AI Engineer, you'll develop and optimize LLMs, collaborating closely with cross-functional teams to enhance detection engineering capabilities and drive new AI initiatives.
Build the AI that makes detection engineers 10x more effective. Create cutting-edge LLM research to power our detection engineering community.
At Detections.ai, we're on a mission to 10x detection engineers. Security teams today are drowning in noisy tools and patchwork workflows. We're changing that—by building intuitive, AI-powered experiences that streamline how detection engineers write, test, and manage detections.
We combine deep expertise in cybersecurity, real-time systems, and applied AI to rethink how detection work gets done—from the ground up. Our team thrives on ownership, speed, and building for real users.
What You'll Do
We're looking for an AI LLM Engineer to drive the development and optimization of large language models that power our intelligent detection engineering products. You'll research, design, and implement cutting-edge LLM capabilities—from retrieval-augmented generation to agentic workflows—bringing AI into the critical path of security operations.
You'll:
- Implement, fine-tune, and optimize state-of-the-art LLMs for performance, accuracy, and security use cases
- Design and evaluate prompting strategies, flows, and application logic for LLM-powered features
- Build and integrate advanced capabilities such as RAG, function calling, and code interpreter technologies
- Design and deploy scalable ML pipelines for both batch and real-time use cases
- Collaborate closely with ML engineers, product teams, and detection engineers to align AI capabilities with business goals
- Lead the incubation of new AI initiatives and drive strategic technology choices in a microservices architecture
- Stay current with the latest research in LLMs, agents, and large-scale training methods, applying insights directly into production systems
- Document methodologies, models, and results to share across the team and company
You're a fit if you:
- Have 5+ years of experience in NLP, machine learning, or data science
- Are strong in Python (R is a plus) and deep learning frameworks (e.g., PyTorch, TensorFlow)
- Have hands-on experience building, testing, and deploying LLMs such as GPT-4, Gemini, or similar
- Understand model training techniques, including data/model parallelism and distributed training
- Are comfortable with cloud-native infrastructure (AWS or GCP) and distributed computing
- Have experience with DevOps/MLOps/LLMOps practices
- Thrive in a high-context, low-process environment
You'll stand out if you:
- Hold a Master's or PhD in Computer Science, AI, or related fields
- Have experience building GenAI solutions using RAG frameworks or LLM agentic applications
- Bring direct experience applying AI in cybersecurity workflows
- Have strong research-to-production skills, bridging advanced ML concepts into deployable systems
Our Stack
- Frontend: React.js, Tailwind CSS, TypeScript
- Middle layer: Node.js, TypeScript
- Backend: Python (FastAPI)
- Infra & DevOps: AWS, GCP, Docker, Terraform, GitHub Actions
- Data: OpenSearch, DynamoDB
- AI Agents: Gemini, Anthropic, OpenAI
- Tooling: Figma, Storybook, CI/CD pipelines
Why Join Us?
- Build for real users: Work hand-in-hand with detection engineers
- Own meaningful problems: Ship features that matter
- Move fast, without red tape: Small, high-context team
- Make an outsized impact: You'll help shape both product and culture
Ready to apply cutting-edge AI research to real-world security challenges? Show us what you've built with LLMs and tell us how AI can revolutionize detection engineering.
- Apply now to help us build the future of AI-powered security tooling.
Similar Jobs
Blockchain • Fintech • Payments • Consulting • Cryptocurrency • Cybersecurity • Quantum Computing
Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. Define technical standards for HPC, GPUs, Kubernetes, private and hybrid cloud, networking, storage, reliability, security, observability, and automation. Solve complex scalability and performance challenges, guide cross-functional initiatives, evaluate emerging technologies, mentor engineers, and embed governance, compliance, privacy, and responsible AI into secure, resilient infrastructure.
Top Skills:
Accelerated ComputingAgentic AiDistributed SystemsGpu ComputingHigh-Performance NetworkingHigh-Performance StorageHpcHybrid CloudInfrastructure AutomationKubernetesLlmsMachine LearningObservabilityPrivate CloudRack-Scale SystemsSre
Computer Vision • Digital Media • Kids + Family • Mobile • Software • Sports
Build and operate production-grade AI developer tooling, agentic workflows, and shared infrastructure that improve software development across engineering teams. Responsibilities include AI-assisted code review, testing, issue triage, model integrations, evaluations, monitoring, cost controls, adoption enablement, and cross-functional collaboration with security, platform, quality, product, and design teams.
Top Skills:
Agentic WorkflowsAi InfrastructureAi SystemsBackend EngineeringDeveloper ToolingEvaluation FrameworksFull-Stack EngineeringLlmsObservabilityRetrievalVendor Integrations
Fintech • Machine Learning • Payments • Software • Financial Services
Design, develop, deploy, and support enterprise AI systems, including foundation models, LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Establish AI architecture standards, optimize model performance, cost, latency, and scalability, and lead model routing and orchestration initiatives. Drive responsible AI practices, contribute to technical roadmaps, mentor engineering teams, and influence cross-functional stakeholders and enterprise AI strategy.
Top Skills:
Agentic AiAi ObservabilityAi OrchestrationAWSAws UltraclustersAzureC#C++CudaData Pipeline GovernanceGoGCPHugging FaceJavaLarge Language ModelsModel CachingModel EvaluationModel RoutingMulti-Agent WorkflowsPythonPyTorchScalaSimilarity SearchVectordbs
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
