About the job
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 - and we need another engineer to join us.
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 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.
Qualifications
Minimum
- 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).
Preferred
- 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.