AI Inference Engineer – High-Performance GPU Systems

Perplexity; California (MO); On-site; USD 120,000 - 170,000; Full time

Job summary

Perplexity is seeking an engineer to join our inference stack. You will work on GPU-accelerated ML inference, supporting transformer models, caching, and low-latency serving. You\'ll collaborate across Rust, Python, CUDA, CuTe DSL, and deploy production distributed systems under real load with a focus on performance and reliability.

You will contribute to a stack built around PyTorch, CUDA kernels, and modern ML tooling, delivering scalable inference in a fast-paced environment.

Qualifications

• 3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems.
• Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow).
• Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores).
• Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation).

Responsibilities

• Develop models support for transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway.
• GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow.
• Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic.
• Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernel interleaving.
• Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents.

Skills

GPU programming

LLM architectures

Distributed systems

Self-directed

Rust/Python/CUDA

Education

Bachelor's degree in Computer Science or related field

Tools

PyTorch

CUDA

Kubernetes

Nsight Compute/Systems

Job description

Perplexity is seeking an engineer to join our inference stack. You will work on GPU-accelerated ML inference, supporting transformer models, caching, and low-latency serving. You\'ll collaborate across Rust, Python, CUDA, CuTe DSL, and deploy production distributed systems under real load with a focus on performance and reliability.

You will contribute to a stack built around PyTorch, CUDA kernels, and modern ML tooling, delivering scalable inference in a fast-paced environment.