About the job
Architect the core platform that powers synthetic data generation, agentic workflows, RL environments, and scalable LLM operations. Design and evolve the APIs, compute services, and orchestration layers that empower internal and customer-facing applications. Generate and refine synthetic data at massive scale. Create and evaluate agentic systems that reason, act, and improve over time. Provision RL training environments and simulation frameworks for next-generation AI agents. Deploy robust benchmarks, datasets, and automated labeling pipelines to accelerate model development. Run LLMs and multi-agent systems efficiently, reliably, and cost-effectively across cloud and hybrid environments.
Responsibilities
Architect the core platform that powers synthetic data generation, agentic workflows, RL environments, and scalable LLM operations
Design and evolve the APIs, compute services, and orchestration layers that empower internal and customer-facing applications
Generate and refine synthetic data at massive scale
Create and evaluate agentic systems that reason, act, and improve over time
Provision RL training environments and simulation frameworks for next-generation AI agents
Deploy robust benchmarks, datasets, and automated labeling pipelines to accelerate model development
Run LLMs and multi-agent systems efficiently, reliably, and cost-effectively across cloud and hybrid environments
Qualifications
Minimum
5 years of experience building customer-facing, cloud-native software systems
Deep experience with AI/ML pipelines, LLM-based systems, or agentic workflows
Experience with distributed computing, large-scale data systems, or orchestration frameworks
Experience at high-growth technology startups
Experience building software products for large enterprise customers
Expertise in Python and cloud platforms (AWS, GCP, or Azure)
Strong understanding of production web-scale systems: monitoring, telemetry, reliability, performance, debugging, and triage
Preferred
No preferred qualifications listed.