Member of Technical Staff (AI Inference Engineer)
Skills & Technologies
Requirements
- Experience
- 3+ years
Benefits
Joblaze summary
The AI Inference Engineer at Perplexity focuses on enhancing the inference engine that powers the company's query responses, managing everything from model support to performance optimization. Key skills include GPU programming, proficiency in Rust and Python, and experience with distributed systems, particularly in high-performance environments. This role is ideal for someone with a strong background in machine learning inference and a self-directed approach to problem-solving. Perplexity's team is dynamic, emphasizing innovation and rapid adaptation in a fast-paced setting.
Joblaze insights
- Listed about a month ago on Joblaze — tracked directly from Perplexity's career page.
- Perplexity has 59 other open roles including Customer Success & Enterprise Support Lead, APAC , Sales Systems Engineer, Enterprise Operations, Scaled Enterprise Customer Success Manager.
- 2358 active Python roles on Joblaze right now.
- 1056 active Kubernetes roles on Joblaze right now.
- 242 active Rust roles on Joblaze right now.
Quick facts
- How much experience is required?
- At least 3 years of relevant experience for this Member of Technical Staff (AI Inference Engineer) role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: NVLink, CuTe DSL, Triton, JAX, CUTLASS, CUDA.
- What seniority level is this role?
- Perplexity targets staff-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Member of Technical Staff (AI Inference Engineer) role at Perplexity.
From the original posting
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.