Founding Machine Learning Infrastructure Engineer

Optimize large-scale LLM inference and serving systems.

Improve total tokens per second, decode tokens per second, latency, throughput, and cost efficiency.

Work on serving infrastructure for open-source models across different types of accelerators.

Improve batching, scheduling, KV cache management, memory usage, and accelerator utilization.

Support long-context inference, including workloads targeting up to 1M context.

Debug performance bottlenecks across model execution, runtime, networking, and infrastructure.

Work with frameworks such as JAX/XLA, PyTorch, vLLM, SGLang, TensorRT-LLM, or related systems.

Collaborate closely with the application team to ensure infrastructure is optimized for agentic workloads, not just generic chatbot inference.

Help turn research prototypes into reliable, high-performance production systems.

Hands-on technical excellence and strong engineering judgment.

End-to-end ownership, from design to implementation to production outcomes.

Bias for action: ship quickly, learn from failures, and iterate.

High intensity during critical milestones, with a focus on real customer impact.

Ability to do deep, focused work and sustain execution.

Clear communication with teammates, customers, and stakeholders.

Comfort with ambiguity, rapid change, and wearing multiple hats.

Low ego, high integrity, high accountability, and strong collaboration.

Continuous learning and a belief that judgment, intelligence, and capability compound over time.

If you are excited to build the infrastructure and agent systems behind the next generation of AI applications, push open-source models to production-grade performance, and turn ambitious research ideas into real-world impact, Model AI is the place for you.