Summary
A rapidly growing AI technology organization is seeking an AI Inference Engineer to develop and optimize the infrastructure powering large-scale model inference. You will support transformer and multimodal models, write and optimize GPU kernels, develop a high-performance Rust serving runtime, and troubleshoot bottlenecks across networking, batching, and GPU execution. The role suits engineers who thrive on deep systems work, production-scale distributed computing, and end-to-end ownership.
Highlights
Deeply technical opportunity focused on high-performance AI inference at scale. Engineers can work across GPU kernels, Rust serving infrastructure, distributed systems, model architectures, performance optimization, and production reliability. The role offers substantial technical ownership and exposure to cutting-edge GPU and AI infrastructure.
Description
We are looking for an AI Inference Engineer to join our growing team.
We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets.
Our stack is Rust, Python, CUDA, and CuTe DSL.
Responsibilities
New models support.
Support 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 kernels interleaving.Reliability and observability.
Build dashboards, alerts, and automated remediation so we catch regressions before users do.
Respond to and learn from production incidents.
Who We're Looking For
Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar).
Any other deep systems programming experience is a plus.You understand modern LLM architectures and are able to bring them up reliably in a production environment.You've built and operated production distributed systems under real load - ideally performance-critical ones.Comfortable working across languages and layers: Rust for the serving runtime, Python for model code, CUDA/CuteDSL for kernels.You own problems end-to-end.
You can read a research paper on Monday, write a kernel on Wednesday, and debug a production incident on Friday.Self-directed.
You do well in fast-moving environments where the path forward isn't laid out for you.
Nice-to-have
ML compilers and framework internals: PyTorch internals, torch.compile, custom operators.Distributed GPU communication: NCCL, NVLink, InfiniBand, RDMA libraries, model/tensor parallelism.Low-precision inference: INT8/FP8/FP4 quantization, mixed-precision serving.Profiling and debugging tools: Nsight Compute/Systems, CUDA-GDB, PTX/SASS analysis.Container orchestration: Kubernetes, GPU scheduling, autoscaling inference workloads.
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).
Final offer amounts are determined by multiple factors including experience and expertise.
Equity: In addition to the base salary, equity may be part of the total compensation package.
