Senior AI/ML Engineer, Cyberhaven

• Proven Backend Expertise: 6+ years of experience building and scaling backend systems, with strong proficiency in Python and/or Go, powering production applications used at scale
• Full-Stack Impact: Demonstrated track record of designing and delivering full-stack applications that are actively used in real-world, high-traffic environments
• Generative AI & ML at Scale: Hands-on production experience with large-scale foundational models and transformer-based architectures, including deploying, monitoring, and iterating on GenAI systems
• Agent & Workflow Systems: At least 1 year of experience building AI agents and orchestration workflows using frameworks such as LangChain, LangGraph, Genkit, or similar technologies
• ML Systems & Data Pipelines: Experience delivering machine learning systems at scale, including robust model evaluation, continuous quality improvement pipelines, and data processing using columnar databases and large messaging systems (e.g., Pub/Sub, Kafka)
• Ownership & Innovation Mindset: Comfortable working in remote, fast-paced environments, with an entrepreneurial mindset-driven to solve complex, real-world problems through innovation and breakthrough technology

Desirable

• We’re looking for an experienced Backend engineer to help drive and evolve our product, focusing on high-impact innovation projects as part of our Linea AI team
• You will be working on cutting-edge technology and ideas and bringing AI prototypes to production
• You will work closely with Labs team to operationalize and scale AI prototypes across many customers
• Design and scale distributed systems: Architect, build, and optimize highly scalable and fault-tolerant systems that process large graph datasets at enterprise scale, handling billions of events in real time from tens of thousands of endpoints with sub-second latency
• Solve complex scaling challenges: Tackle real-world performance and reliability problems through deep analysis, profiling, and systematic troubleshooting in high-throughput distributed environments
• Build with modern infrastructure: Develop and evolve a microservices-based architecture using technologies such as Go, Kubernetes, Docker, and Redis, in a continuously improving production stack
• Engineer secure-by-design software: Write hardened, security-first code capable of safely processing untrusted data, resisting real-world attack vectors, and operating reliably across large-scale, internet-facing systems
• Productionize AI systems: Partner with research and product teams to operationalize AI prototypes, turning experimental models into robust, scalable, and production-ready services