Founding AI Engineer

About Us

VizopsAI is the secure runtime for custom enterprise software. We provide the production layer that turns AI-generated internal tools into compliant, hardened applications — wrapping raw AI code in enterprise-grade identity, security, and infrastructure best practices. We're a lean, fast-moving team building the industrialization layer for the AI app revolution. We're an early stage venture-backed AI-native startup based in the SF Bay Area. The founding team combines deep AI/ML research leadership at Google DeepMind, Amazon Alexa, and Oracle Cloud with AI Product leadership at Verkada, AWS and Sony. Technical leadership includes PhDs from Johns Hopkins specializing in deep learning and optimization. We already have multiple customers locked in and are bringing on rockstars to build the infrastructure that makes enterprise AI adoption safe and scalable.

About the Role

You'll work across the stack to design, train, and deploy RL-driven optimization loops for AI agents. You need to be deeply technical, hands-on, and comfortable moving between research, ML engineering and production (APIs, infra, SLAs).

What You'll Do

• Design RL loops for multi-step, tool-using agents (planning, retrieval, coordination)
• Build backend services for training, evals, and online policy updates
• Train reward models from traces/preferences; run A/Bs & interleavings safely
• Scale distributed training/serving
• Turn papers → running code → measurable uplift
• Partner with product & customers: translate messy, real-world objectives into measurable rewards and robust policies

What We're Looking For

• Strong programming in Python; bonus for TypeScript/Go/Rust for systems and APIs
• LLM systems intuition including tool-use, planning, retrieval, structured outputs, and how evals/telemetry become learning signals
• You've shipped production systems and can debug/profile at speed
• Experimentation mindset: design clean evals, run ablations, read papers, and turn them into maintainable code
• Data & infra fluency: event/trace pipelines, schema design, reproducibility, and versioning for datasets, policies, and rewards
• Clear communication in a fast, ambiguous environment
• 2+ years experience building ML systems

Nice to Have

• RL proficiency with some real-world RL applications
• Experience with agent stacks: orchestration graphs, tool routers, retrievers, evaluation frameworks, and observability of traces
• LLM post-training experience: reward-modeling, preference data collection, safety/guardrail integration, structured evals