Qureos Mentor

Perplexity

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 About Perplexity AI
 
 
 
 Perplexity is an AI-powered answer engine built to serve the world&rsquo;s curiosity with fast, trustworthy answers grounded in the live web and backed by clear citations. It combines multiple leading models with real-time search to synthesize up-to-date, source-linked responses instead of traditional search results. On top of this foundation, Perplexity is rolling out Computer, a general-purpose AI worker that can use software like a human to research, build, and execute end-to-end workflows for users.
 
 
 
 About The Role
 
 
 
 The Cloud Infrastructure team owns the foundational cloud primitives and deployment models that power Perplexity's products, from multi-tenant public cloud to single-tenant and on-premises solutions for enterprise customers.
 
 
 As Perplexity grows its Computer and Enterprise products, this team builds and operates the security, isolation, and compliance layers that customers depend on. We provide the deployment topologies, multi‑region infrastructure, and core services that enable Perplexity to run reliably and efficiently for both consumer traffic and large enterprises.
 
 
 
 Key Responsibilities
 
 
 
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 Own the roadmap and technical strategy for agent-driven cloud infrastructure management.
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 Design and operate Perplexity&rsquo;s cloud networking fabric, including VPC architectures, private connectivity, and peering with hyperscalers and neocloud providers to support low-latency, high-throughput AI workloads.
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 Architect and scale compute platforms (Kubernetes/EKS, autoscaling groups, and mixed CPU/GPU fleets) to efficiently serve online request traffic and background workloads across regions.
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 Build and maintain secure, isolated deployment topologies for multi-tenant, single-tenant, and customer‑owned cloud (BYOC) environments, including cross-account networking, identity, and policy guardrails.
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 Implement and evolve multi-region strategies for availability, failover, and data locality, including traffic routing, regional capacity planning, and disaster recovery playbooks.
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 Partner with security to deliver enterprise controls such as BYOK/KMS integrations, network isolation, and auditability required for regulated customers.
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 Develop automation, tooling, and runbooks that make day‑2 operations (provisioning, upgrades, incident response) predictable and repeatable for Perplexity products across all environments.
 
 
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 Qualifications
 
 
 
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 Deep experience designing and operating cloud infrastructure on AWS (VPC design, routing, security groups, load balancing, private connectivity).
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 Strong background with Kubernetes/EKS and container orchestration, including multi‑cluster, multi‑region, or multi‑account setups.
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 Hands-on experience with cloud networking and peering (VPC peering, Transit Gateway, private link/service endpoints, or similar constructs in neoclouds).
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 Experience building or operating secure, isolated environments for enterprise customers (single‑tenant, BYOC, or on‑prem), ideally including BYOK/KMS integrations and compliance constraints.
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 Proficiency with infrastructure as code (Terraform) and strong software engineering skills in at least one of Python, Go, or Rust for automation and tooling.
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 Strong debugging and incident management skills across distributed systems (networking, compute, and platform layers), with a track record of driving root‑cause analysis and long‑term fixes.
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 7+ years of industry experience building and operating production cloud infrastructure, including leading the design of complex systems or migrations.
 
 
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 If you&rsquo;re excited about this role, we encourage you to apply even if your experience doesn&rsquo;t match every qualification listed above.
 
 
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Member of Technical Staff (Software Engineer, Cloud Infrastructure)

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 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
 
 
 
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 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.
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 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.
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 Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic.
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 Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernels interleaving.
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 Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents.
 
 
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 Who We're Looking For
 
 
 
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 Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). Any other deep systems programming experience is a plus.
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 You understand modern LLM architectures and are able to bring them up reliably in a production environment.
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 You've built and operated production distributed systems under real load - ideally performance-critical ones.
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 Comfortable working across languages and layers: Rust for the serving runtime, Python for model code, CUDA/CuteDSL for kernels.
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 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.
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 Self-directed. You do well in fast-moving environments where the path forward isn't laid out for you.
 
 
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 Nice-to-have
 
 
 
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 ML compilers and framework internals: PyTorch internals, torch.compile, custom operators.
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 Distributed GPU communication: NCCL, NVLink, InfiniBand, RDMA libraries, model/tensor parallelism.
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 Low-precision inference: INT8/FP8/FP4 quantization, mixed-precision serving.
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 Profiling and debugging tools: Nsight Compute/Systems, CUDA-GDB, PTX/SASS analysis.
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 Container orchestration: Kubernetes, GPU scheduling, autoscaling inference workloads.
 
 
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 Qualifications
 
 
 
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 3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems.
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 Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow).
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 Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores).
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 Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation).
 
 
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 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.
 
 
 
</div>

Member of Technical Staff (AI Inference Engineer)

<div>
 Perplexity is looking for an experienced Product Data Scientist to accelerate the development of advanced search technologies. You will identify robust and sensitive signals from user behavior to help us gather insights from A/B experiment data more efficiently.
 
 
 
 Responsibilities
 
 
 
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 Develop data-driven insights from user behavior to inform our product roadmap and accelerate adoption
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 Formulate hypotheses and validate them by designing, running, and analyzing A/B tests
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 Determine appropriate metrics and visualizations for tracking, and implement them in dashboards
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 Design new pipelines that will help to deliver better ranking quality. From discovering new signals, producing metrics and construct data labeling pipelines with human and LLM feedback
 
 
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 Qualifications
 
 
 
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 4+ years of experience working as a data analyst or in a related role
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 Experience working on search-related products, with emphasis on designing online metrics and analyzing A/B experiments
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 Strong Python skills (expected to write production-grade code)
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 Proficiency with SQL
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 Experience with Business Intelligence (BI) tools
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 Deep knowledge of statistics
 
 
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 Preferred Qualifications
 
 
 
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 Proficiency with Apache Spark
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 Experience with Databricks
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 Experience with development of LLM-as-a-judge systems
 
 
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Member of Technical Staff (Product Data Scientist, Search Quality)

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 Perplexity is seeking an experienced Machine Learning Engineer to help build the next generation of advanced search technologies, with a focus on retrieval and ranking.
 
 
 
 Responsibilities
 
 
 
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 Relentlessly push search quality forward&mdash;through models, data, tools, or any other leverage available
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 Architect and build core components of our search platform and model stack
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 Train and evaluate retrieval, ranking and classification models, including LLMs
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 Deploy models - from boosting to LLMs - in a scalable and performant way
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 Build and optimize RAG pipelines for grounding and answer generation
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 Collaborate with Data, AI, Infrastructure and Product teams to ensure fast and high quality delivery
 
 
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 Qualifications
 
 
 
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 Deep understanding of search and retrieval systems, including quality evaluation principles and metrics
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 Proven track record with large-scale search or recommender systems
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 Self-driven, with a strong sense of ownership and execution
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 Minimum of 5 years of working on search or recsys-related projects
 
 
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Member of Technical Staff (Machine Learning Engineer, Search)

Join Perplexity and explore 4   exciting career opportunities. They're hiring for various roles with options for remote or on-site work. Apply today and become part of their innovative team.

Perplexity

About Company

Member of Technical Staff (Software Engineer, Cloud Infrastructure)

Member of Technical Staff (AI Inference Engineer)

Member of Technical Staff (Product Data Scientist, Search Quality)

Member of Technical Staff (Machine Learning Engineer, Search)