Founding Engineer, AI Infra

Key responsibilities

• AI Performance Efficiency: Improve LLM training and inference efficiency through better memory utilization, optimized parallelism, and kernel-level innovations to serve frontier models in both GPU-poor and GPU-rich scenarios.
• Training stability & RL robustness: Build scalable, stable training and RL pipelines with strong reproducibility, observability, and debuggability.
• System-aware co-design: Prototype research ideas directly in training and inference stacks (e.g., parallelism strategies, attention kernels, RL training pipelines) and validate them at scale.
• Scalability & Infrastructure: Own end-to-end training and inference infrastructure — from data ingestion and checkpointing to multi-node and multi-cloud orchestration.
• Production enablement: Work closely with researchers and product engineers to turn new algorithms into reliable, production-ready systems.

Requirements

• 5+ years building or operating ML infrastructure at scale, ideally supporting large language or multimodal models.
• Hands-on experience running inference stacks (vLLM / SGLang, TGI, Triton) and optimizing them via low-level profiling.
• Strong software engineering fundamentals in Python and one of C++/Rust/Go, with clean, reliable code shipped to production.
• Working knowledge of modern data pipelines, feature stores, and vector databases used in production AI systems.
• Comfort automating infrastructure with Kubernetes, Terraform/Pulumi, and observability stacks (Prometheus, Grafana, OpenTelemetry).

Bonus Points

• Experience deploying open-source LLMs (Qwen, DeepSeek, Kimi, GLP, Llama etc) or training custom foundation models in coding, reasoning, agent etc.
• Contributions to AI/ML systems tooling (compilers, kernels, inference runtimes) or open-source infrastructure projects.
• Background in RL, SFT, PEFT / LoRA, training data processing, evaluation, agent harnesses, sandbox environment / tool optimizations that hardens the end-to-end production AI systems.