Member of Technical Staff, AI Infrastructure

Goaly AI is building AI systems that reason, use tools, and complete meaningful work through advances in model post-training, reinforcement-learning infrastructure, large-scale training, and product engineering. The Member of Technical Staff will identify performance and reliability bottlenecks in large distributed AI workloads and build durable infrastructure improvements across training, inference, reinforcement learning, and GPU cluster platforms.

Responsibilities:

• Profile end-to-end AI workloads and identify limiting resources across model code, GPU kernels, memory, collective communication, networking, storage, orchestration, and environment execution
• Build low-latency, high-throughput sampling and inference systems for large language models, including batching, scheduling, caching, load balancing, and efficient weight updates
• Optimize GPU execution through kernel and graph profiling, memory-layout improvements, reduced-precision computation, communication overlap, compilation, and targeted CUDA or Triton work
• Improve distributed training and reinforcement-learning performance across heterogeneous GPU and CPU workloads, variable-length rollouts, complex network topologies, and changing model architectures
• Design quantitative performance and capacity models that predict bottlenecks, explain scaling behavior, guide hardware and topology choices, and prioritize engineering work
• Build scheduling and load-balancing mechanisms that improve accelerator utilization while respecting memory, locality, topology, latency, and fault-domain constraints
• Design fault-tolerant distributed systems that detect failures early, isolate their impact, recover efficiently, and preserve correctness during long-running jobs
• Investigate difficult production issues such as kernel-level stalls, network-latency spikes, collective timeouts, memory fragmentation, stragglers, and performance regressions in containerized environments
• Develop benchmarks, profiling tools, performance dashboards, and regression tests that make system behavior visible and allow improvements to be measured under realistic workloads
• Partner with researchers to understand new models and algorithms, remove infrastructure constraints from the experimental loop, and translate successful optimizations into reusable platform capabilities