Your work will touch the full GenAI inference stack , from kernels and runtimes to orchestration and memory management. You will contribute to the design and implementation of the inference engine, and collaborate on model-serving stack optimized for large-scale LLMs inference.

Key responsibilities include:

• Collaborating with researchers to bring new model architectures or features (sparsity, activation compression, mixture-of-experts) into the engine
• Optimizing for latency, throughput, memory efficiency, and hardware utilization across GPUs, and accelerators
• Building and maintaining instrumentation, profiling, and tracing tooling to uncover bottlenecks and guide optimizations
• Developing and enhancing scalable routing, batching, scheduling, memory management, and dynamic loading mechanisms for inference workloads
• Supporting reliability, reproducibility, and fault tolerance in the inference pipelines, including A/B launches, rollback, and model versioning
• Integrating with federated, distributed inference infrastructure – orchestrate across nodes, balance load, handle communication overhead
• Collaborating cross-functionally: with platform engineers, cloud infrastructure, and security/compliance teams
• Documenting and sharing learnings, contributing to internal best practices and open-source efforts when possible

Requirements include:

• BS/MS/PhD in Computer Science, or a related field
• Strong software engineering background (3+ years or equivalent) in performance-critical systems
• Solid understanding of ML inference internals: attention, MLPs, recurrent modules, quantization, sparse operations, etc.
• Hands-on experience with CUDA, GPU programming, and key libraries (cuBLAS, cuDNN, NCCL, etc.)
• Comfortable designing and operating distributed systems, including RPC frameworks, queuing, RPC batching, sharding, memory partitioning
• Demonstrated ability to uncover and solve performance bottlenecks across layers (kernel, memory, networking, scheduler)
• Experience building instrumentation, tracing, and profiling tools for ML models
• Ability to work closely with ML researchers, translate novel model ideas into production systems
• Ownership mindset and eagerness to dive deep into complex system challenges
• Bonus: published research or open-source contributions in ML systems, inference optimization, or model serving

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